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The Flow of Association and Causation in Graphs Brady Neal causalcourse.com

The Flow of Association and Causation in Graphs

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Page 1: The Flow of Association and Causation in Graphs

The Flow of Association and Causation in Graphs

Brady Neal

causalcourse.com

Page 2: The Flow of Association and Causation in Graphs

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Graph terminology

Bayesian networks and causal graphs

The basic building blocks of graphs

The flow of association and causation

Page 3: The Flow of Association and Causation in Graphs

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Graph terminology

Bayesian networks and causal graphs

The basic building blocks of graphs

The flow of association and causation

Graph terminology

Page 4: The Flow of Association and Causation in Graphs

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Graph terminology: not a graph

4Graph terminology

Page 5: The Flow of Association and Causation in Graphs

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x

y

Graph terminology: not a graph

4Graph terminology

Page 6: The Flow of Association and Causation in Graphs

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x

y

Graph terminology: not a graph

4Graph terminology

Page 7: The Flow of Association and Causation in Graphs

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x

y

Graph terminology: not a graph

4Graph terminology

Page 8: The Flow of Association and Causation in Graphs

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Graph terminology: Terminology Machine Gun

5

termterm

termtermterm

termtermterm

Graph terminology

Page 9: The Flow of Association and Causation in Graphs

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Graph terminology

6Graph terminology

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Graph terminology

6

A B

C D

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Graph terminology

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Graph terminology

6

NodesA B

C D

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Node Node

Node Node

Graph terminology

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Graph terminology

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A B

C D

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Edge

Edge

Edge

Edg

eEdges

Graph terminology

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Graph terminology

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A B

C D

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UndirectedGraph

Graph terminology

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DirectedGraph

A B

C D

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Graph terminology

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DirectedGraph

A B

C D

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Parent Child

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Graph terminology

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6

A B

C D

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Cycle

Graph terminology

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A B

C D

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Graph terminology

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Graph terminology

6

A B

C D

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Directed Acyclic Graph (DAG)

Graph terminology

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Graph terminology

6

Directed Acyclic Graph (DAG)

A B

C D

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Graph terminology

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Graph terminology

6

Directed Acyclic Graph (DAG)

A B

C D

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Immorality

Graph terminology

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Graph terminology

Bayesian networks and causal graphs

The basic building blocks of graphs

The flow of association and causation

Bayesian networks and causal graphs

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Naively modeling the joint distributionStatistical modeling (no causality):

8Bayesian networks and causal graphs

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Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

Bayesian networks and causal graphs

Page 31: The Flow of Association and Causation in Graphs

Brady Neal / 35

Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

Bayesian networks and causal graphs

Page 32: The Flow of Association and Causation in Graphs

Brady Neal / 35

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

Bayesian networks and causal graphs

Page 33: The Flow of Association and Causation in Graphs

Brady Neal / 35

x1 x2 x3 P (x4 | x3, x2, x1)

0 0 0 ↵1

0 0 1 ↵2

0 1 0 ↵3

0 1 1 ↵4

1 0 0 ↵5

1 0 1 ↵6

1 1 0 ↵7

1 1 1 ↵8

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P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

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Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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Bayesian networks and causal graphs

Page 34: The Flow of Association and Causation in Graphs

Brady Neal / 35

x1 x2 x3 P (x4 | x3, x2, x1)

0 0 0 ↵1

0 0 1 ↵2

0 1 0 ↵3

0 1 1 ↵4

1 0 0 ↵5

1 0 1 ↵6

1 1 0 ↵7

1 1 1 ↵8

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P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

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Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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2n�1

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parameters!

Bayesian networks and causal graphs

Page 35: The Flow of Association and Causation in Graphs

Brady Neal / 35

x1 x2 x3 P (x4 | x3, x2, x1)

0 0 0 ↵1

0 0 1 ↵2

0 1 0 ↵3

0 1 1 ↵4

1 0 0 ↵5

1 0 1 ↵6

1 1 0 ↵7

1 1 1 ↵8

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P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

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Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

<latexit sha1_base64="UyTyA1Kdh3gJGoeUrvSEJndpAjw=">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</latexit>

2n�1

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parameters!

X1 X2

X3 X4

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Bayesian networks and causal graphs

Page 36: The Flow of Association and Causation in Graphs

Brady Neal / 35

x1 x2 x3 P (x4 | x3, x2, x1)

0 0 0 ↵1

0 0 1 ↵2

0 1 0 ↵3

0 1 1 ↵4

1 0 0 ↵5

1 0 1 ↵6

1 1 0 ↵7

1 1 1 ↵8

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P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

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Naively modeling the joint distributionStatistical modeling (no causality):

8

P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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P (x1, x2, . . . , xn) = P (x1)Y

i

P (xi | xi�1, . . . , x1)

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2n�1

<latexit sha1_base64="+9ySUnU6KlrKQKB0EV5LG7T9HnY=">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</latexit>

parameters!

X1 X2

X3 X4

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Bayesian networks and causal graphs

Page 37: The Flow of Association and Causation in Graphs

Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9Bayesian networks and causal graphs

Page 38: The Flow of Association and Causation in Graphs

Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9

X1 X2

X3 X4

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P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

Bayesian networks and causal graphs

Page 39: The Flow of Association and Causation in Graphs

Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9

X1 X2

X3 X4

<latexit sha1_base64="XWIFaVq5D6n8XxXfE9BPk69Sd8E=">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</latexit>

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3)

<latexit sha1_base64="g1YWXRc0AshDxgtghfw26bgDa8c=">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</latexit>

Bayesian networks and causal graphs

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Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3)

<latexit sha1_base64="g1YWXRc0AshDxgtghfw26bgDa8c=">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</latexit>

X1 X2

X3 X4

<latexit sha1_base64="XmBsWAmtXw3NDfG6PRb34AM0Nb8=">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</latexit>

Bayesian networks and causal graphs

Page 41: The Flow of Association and Causation in Graphs

Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3, x2, x1)

<latexit sha1_base64="o+p/T7L1m/dcl7x2u6T1WnR8yhE=">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</latexit>

P (x1, x2, x3, x4) = P (x1)P (x2 | x1)P (x3 | x2, x1)P (x4 | x3)

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X1 X2

X3 X4

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Question:How will the factorization change now?

Bayesian networks and causal graphs

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Brady Neal / 35

Local Markov assumptionGiven its parents in the DAG, a node X is independent of all of its non-descendants.

9

P (x1, x2, x3, x4) = P (x1)P (x2)P (x3 | x1)P (x4 | x3)

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X1 X2

X3 X4

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Question:How will the factorization change now?

Bayesian networks and causal graphs

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Brady Neal / 35

Bayesian network factorization

10Bayesian networks and causal graphs

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Bayesian network factorization

10

P (x1, . . . , xn) =Y

i

P (xi | pai)

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Bayesian networks and causal graphs

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Bayesian network factorization

10

P (x1, . . . , xn) =Y

i

P (xi | pai)

<latexit sha1_base64="Pa7R4s6Fv20mxBNTW+wcVpyCDVE=">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</latexit>

local Markov assumption Bayesian network factorization =)

<latexit sha1_base64="WuvX0VO9o9dAwOcsTKhIQj9C2aw=">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</latexit>

Bayesian networks and causal graphs

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Brady Neal / 35

Bayesian network factorization

10

P (x1, . . . , xn) =Y

i

P (xi | pai)

<latexit sha1_base64="Pa7R4s6Fv20mxBNTW+wcVpyCDVE=">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</latexit>

local Markov assumption Bayesian network factorization =)

<latexit sha1_base64="WuvX0VO9o9dAwOcsTKhIQj9C2aw=">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</latexit>

local Markov assumption Bayesian network factorization =)

<latexit sha1_base64="WuvX0VO9o9dAwOcsTKhIQj9C2aw=">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</latexit>

Bayesian networks and causal graphs

Page 47: The Flow of Association and Causation in Graphs

Brady Neal / 35

Bayesian network factorization

10

P (x1, . . . , xn) =Y

i

P (xi | pai)

<latexit sha1_base64="Pa7R4s6Fv20mxBNTW+wcVpyCDVE=">AAAI3nicfVVLbxs3EN6kj6juy2mPvbB1DCTAWpUUA04PAgLYDoIiARzArzZrKFwu1yLEF0iuZZnYa29Fb0F/TK/tf+i/6ZBax9LaKgVJw/m+GQ6Hw2GuObOu1/v33v2PPv7k0wedz9Y+/+LLr75ef/jNsVWVIfSIKK7MaY4t5UzSI8ccp6faUCxyTk/yyW7ATy6osUzJQzfT9Ezgc8lKRrAD1Wi9l+nHl6N+irJCOZuiy5F8goYo00YVI4YiCn+CFSDjEXsyWt/odXtxoNtCvxE2kmYcjB4+eA++SSWodIRja9/2e9qdeWwcI5zWa1llqcZkgs/pWxAlFtSe+bi1Gm2CpkClMvCVDkXtooXHwtqZyIEpsBvbNhaUd2FvK1c+O/NM6spRSeYLlRVHTqGQJ1QwQ4njMxAwMQxiRWSMDSYOsrm2tEwu6uW5UhOHc1sjtIleQOiSEYpAw+lyfJcl+FtmBU04GVhiEx2yyRVqyMuW8+wsqRyQ6w9WljrH5LlFSjsm2FWTRIIrizk6N1iPbRfIP1c2ZFXPtjS2DqIcMwtLGhd9B5+c5QabmbdjrKlNC0qUicVjU2yMmtqUYE4auSuow2nJXKqVZYEFQUCcwRG482sIxtYr7OhliiunUqmKkGvrsITdD/uICIRlgYKQWioYBEQmabQDkqM/dq2bcaB6yjnTlqaoMHiaIsEkE5VAU1a4MZRwr7sDPuq5qVZMug+myBNmoPauTZmU1EDK9BAKeRsWLhnn89AIVB0Urh36unGVs3lp0KLxN/RxP82uCmzHtGi4lC+sebPKQLsUYc7O5ZDTEmTLlQajtXh+u3AkSsDKQkAm4NwlnTYTn+3XPgvFnOd+v66XsWNsGtQIHyYtnMmCamBoajTKvg8fFCctnrwmSuWA9P/0uVcKP9L50DYc5CYGoTGHIqQ3yhvmYRNFiLCg5SKy0d8YNJug3GeGY+0fBeWjOhMTauRAVD7M6/b29hR4ZaUQ4dyiB+Z8oeqMcgsKOBzXKErWMtX4JnEgt9CC3qAgt1CiLrBhcPa29qctzEErdqHx1f6wBanKgQyef7nlT5aqglwYcHjSAgWFduTg6vvXbX9uTM1FqIBfVwZ4gXntL1fFGFF3d5gRm62ONOLTFcFGUKyIN4JXLZBaaFog1f5N+6RqfwC3JFyT0AJtrB94+2JD9FpNoarCXd8ycJ/8y8PXr2q/+3S7//RFvZKa84pecwe7O3t7g9VcDa12a959G4v9nfCBmLKohpr3i6S7bRfIkMXiulNeG0FrGDto0rMWMaZ7gdfa7wIZ3llFWOzTq8jwkvfb7/Zt4XjQ7W93f3qzvfH8WfOmd5Lvkh+Sx0k/2UmeJy+Tg+QoIcn75K/k7+SfzrvOb53fO3/MqffvNTbfJkuj8+d/zawwJw==</latexit>

local Markov assumption Bayesian network factorization =)

<latexit sha1_base64="WuvX0VO9o9dAwOcsTKhIQj9C2aw=">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</latexit>

local Markov assumption Bayesian network factorization =)

<latexit sha1_base64="WuvX0VO9o9dAwOcsTKhIQj9C2aw=">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</latexit>

See Chapter 3 of Koller & Friedman (2009) book for proofs

Bayesian networks and causal graphs

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Brady Neal / 35

Minimality assumption

11Bayesian networks and causal graphs

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Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).

11Bayesian networks and causal graphs

Page 50: The Flow of Association and Causation in Graphs

Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).

11

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

Bayesian networks and causal graphs

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Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).

11

P (x, y) = P (x)P (y | x)

<latexit sha1_base64="tpC76sK9c2GgSDRHdgsRIlla8do=">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</latexit>

Permits distributions where

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

Bayesian networks and causal graphs

Page 52: The Flow of Association and Causation in Graphs

Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).

11

P (x, y) = P (x)P (y | x)

<latexit sha1_base64="tpC76sK9c2GgSDRHdgsRIlla8do=">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</latexit>

P (x, y) = P (x)P (y)

<latexit sha1_base64="twfk0h+aoD5xg0dFMJLihiK9v4o=">AAAIx3icfVXbbhs3EN2kl6juJU77VPSFrWMgBtaqpBhw+iAggO0gLRLAAXxrLcPgcmctQryB5NqWiUXRn+gf9LX9nv5Nh6t1LG2sciFhOOfMcDgcDjMjuPO93r8PHn708SefPup8tvL5F19+9Xj1yddHTpeWwSHTQtuTjDoQXMGh517AibFAZSbgOJvsRPz4EqzjWh34qYEzSS8ULzijHlXnq9+OzLPrlEw3yJBEcYOM0ihMN85X13rdXj3Ih0K/EdaSZuyfP3n05yjXrJSgPBPUudN+z/izQK3nTEC1MiodGMom9AJOUVRUgjsL9R4qso6anBTa4k95UmvnLQKVzk1lhkxJ/di1sai8DzstffHiLHBlSg+KzRYqSkG8JjEhJOcWmBdTFCizHGMlbEwtZR7TtrKwTCarxbnWE08zVxGyTl5h6IozIKgRsBjfdYH+FllRE48Al1gnB3xyQxryouUsOwsqj+TqvZUD77m6cEQbzyW/aZLIaOmoIBeWmrHrIvmX0sWsmummoc5jlGPucEnra9/Rp+CZpXYa3JgacGkOTNu6SlxKrdVXLmVUsEbuSvA0LbhPjXY8sjAIjDM6QndhheDYfEM9XKe09DpVOo+5dp4q3P2wT5gkVOUkCqkDyTEgNklrOyR5+LHr/FQgNYAQ3DhISW7pVUokV1yWklzx3I+xbHvdbfRRzUyN5sq/NyWBcYu1d2vKlQKLKTNDLOQtXLjgQsxCY1h1WLhuGKrGVcZnpQF5428Y6v00u8qpG0PecEHMrXm3ysD4lFDBL9RQQIGyE9qg0Up9fjt4JFriylJiJvDcFVw1kzDaq8IoFnOWhb2qWsSOqG1QK0OctHCucjDIMGANGX0fP1JPWjx1S1TaI+n/6TOvgH/Ko2urPeamDsJQgUUId8o75kETRYwwh2IeWeuvDZpNgAgjK6gJT6PyaTWSE7BqIMsQ51V7e7savfJCynhutQfuQ66rEQiHCjwc3ygK3jI19C5xKLfQHO5QlFso05fUcjx7V4WTFuax5/rY+Kpw0IJ06VFGz79+4E8VusRcWHR43AIlYDvyePXD27Y/PwZ7GSvgt6UBXlJRhetlMdaovz/MGpsuj7TGr5YEW4NySbw1eNMCwWHTQqkK79onVYV9vCXxmsQW6Or6wUeubojB6CusqnjXNy3ep/D64O2bKuw83+o/f1UtpWaihFvuYGd7d3ewnGuw1W7Oum9jsbcdP4xpVKux5sM86X7bOTJmMb/tlLdG2BrGHpv0tEWs0z3Ha+13jozvrGa87tPLyPiS99vv9ofC0aDb3+r+9G5r7eWL5k3vJN8lPyTPkn6ynbxMXif7yWHCkt+Tv5K/k386P3d057JzPaM+fNDYfJMsjM4f/wHQXCZV</latexit>

Permits distributions where

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

and also where

Bayesian networks and causal graphs

Page 53: The Flow of Association and Causation in Graphs

Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).2. Adjacent nodes in the DAG are dependent.

11

P (x, y) = P (x)P (y | x)

<latexit sha1_base64="tpC76sK9c2GgSDRHdgsRIlla8do=">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</latexit>

P (x, y) = P (x)P (y)

<latexit sha1_base64="twfk0h+aoD5xg0dFMJLihiK9v4o=">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</latexit>

Permits distributions where

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

and also where

Bayesian networks and causal graphs

Page 54: The Flow of Association and Causation in Graphs

Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).2. Adjacent nodes in the DAG are dependent.

11

P (x, y) = P (x)P (y | x)

<latexit sha1_base64="tpC76sK9c2GgSDRHdgsRIlla8do=">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</latexit>

P (x, y) = P (x)P (y)

<latexit sha1_base64="twfk0h+aoD5xg0dFMJLihiK9v4o=">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</latexit>

Permits distributions where

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

and also where

Bayesian networks and causal graphs

Page 55: The Flow of Association and Causation in Graphs

Brady Neal / 35

Minimality assumption1. Given its parents in the DAG, a node X is independent of all its non-

descendants (local Markov assumption).2. Adjacent nodes in the DAG are dependent.

11

P (x, y) = P (x)P (y | x)

<latexit sha1_base64="tpC76sK9c2GgSDRHdgsRIlla8do=">AAAIznicfVXbbhs3EN2kl6juzWkf+8LWMRADa1VSDDh9EBDAdhAUCeAAvrVew+ByuRIh3kByLcvEom9Fv6Bf0df2X/o3HVLrWFpb5ULCcM6Z4XA4HOaaM+t6vX8fPf7o408+fdL5bO3zL7786uv1p9+cWFUZQo+J4sqc5dhSziQ9dsxxeqYNxSLn9DSf7AX89Ioay5Q8cjNNLwQeSVYygh2oLtdRpp9fp2i2hYZR3EJZGoQZygQr0PXW5fpGr9uLA90X+o2wkTTj8PLpkz+zQpFKUOkIx9ae93vaXXhsHCOc1mtZZanGZIJH9BxEiQW1Fz5upUaboClQqQz8pENRu2jhsbB2JnJgCuzGto0F5UPYeeXKlxeeSV05Ksl8obLiyCkU8oIKZihxfAYCJoZBrIiMscHEQfbWlpbJRb08V2ricG5rhDbRawhdMkIRaDhdju+6BH/LrKAJJwFLbKIjNrlBDXnZcp6dJZUDcv3BylLnmBxZpLRjgt00SSS4spijkcF6bLtA/rmyIat6tq2xdRDlmFlY0rjoO/jkLDfYzLwdY01tWlCiTCwWm2Jj1NSmBHPSyF1BHU5L5lKtLAssCALiDI7AnV9DMLbfYkevU1w5lUpVhFxbhyXsfthHRCAsCxSE1FLBICAySaMdkBz9sWvdjAPVU86ZtjRFhcHTFAkmmagEmrLCjaF6e91d8FHPTbVi0n0wRZ4wA7V3a8qkpAZSpodQyDuwcMk4n4dGoOqgcO3Q142rnM1LgxaNv6GP+2l2VWA7pkXDpXxhzbtVBtqlCHM2kkNOS5AtVxqM1uL57cGRKAErCwGZgHOXdNpMfHZQ+ywUc577g7pexk6waVAjfJi0cCYLqoGhqdEo+z58KE5aPHlLlMoB6f/pc68U/qQD10Y5yE0MQmMORUjvlHfMoyaKEGFBy0Vko78xaDZBuc8Mx9o/C8pndSYm1MiBqHyY1+3t7SvwykohwrlFD8z5QtUZ5RYUcDiuUZSsZarxXeJAbqEFvUNBbqFEXWHD4Oxt7c9amIPW60Ljq/1RC1KVAxk8/3LPnyxVBbkw4PC0BQoK7cjB1ffv2v7cmJqrUAG/rgzwCvPaX6+KMaLu4TAjNlsdacSnK4KNoFgRbwRvWiC10LRAqv379knV/hBuSbgmoQXaWD/w1sWG6LWaQlWFu75t4D75N0fv3tZ+78VO/8XreiU15xW95Q72dvf3B6u5Glrt9rz7NhYHu+GDmLKohpr3i6SHbRfIkMXitlPeGkFrGDto0rMWMaZ7gdfa7wIZ3llFWOzTq8jwkvfb7/Z94WTQ7e90f3q/s/HqZfOmd5Lvkh+S50k/2U1eJW+Sw+Q4IcnvyV/J38k/ncPOVafu/DanPn7U2HybLI3OH/8BVN8o6Q==</latexit>

P (x, y) = P (x)P (y)

<latexit sha1_base64="twfk0h+aoD5xg0dFMJLihiK9v4o=">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</latexit>

Permits distributions where

X Y

<latexit sha1_base64="HVAEvz/e4TxarMZ/wAywSUrLMbo=">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</latexit>

and also where

X Y

<latexit sha1_base64="Iihe2mAXFSKknOF/2cfY1d7fVyc=">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</latexit>

Bayesian networks and causal graphs

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Assumptions flowchart

12

StatisticalIndependencies

StatisticalDependencies

MarkovAssumption

MinimalityAssumption

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Bayesian networks and causal graphs

Page 57: The Flow of Association and Causation in Graphs

Recall:1. How is the local Markov assumption related to the

Bayesian network factorization?2. What are the two parts of the minimality

assumption? What do we gain with the second part?

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Brady Neal / 35

What is a cause?

A variable X is said to be a cause of a variable Y if Y can change in response to changes in X.

14Bayesian networks and causal graphs

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Causal edges assumption

15

In a directed graph, every parent is a direct cause of all its children.

Bayesian networks and causal graphs

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Assumptions flowchart

16

StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

<latexit sha1_base64="6tPs94luz2nsptV6RBAYN3eN3IQ=">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</latexit>

Bayesian networks and causal graphs

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Assumptions flowchart

16

StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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Two assumptions to give us flow of association and causation in graphs:1. Markov Assumption2. Causal Edges Assumption

DAG +

Bayesian networks and causal graphs

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Graph terminology

Bayesian networks and causal graphs

The basic building blocks of graphs

The flow of association and causation

The basic building blocks of graphs

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Graphical building blocks

18The basic building blocks of graphs

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Two nodes:

The basic building blocks of graphs

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Two nodes: or

The basic building blocks of graphs

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Chain

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Two nodes: or

The basic building blocks of graphs

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Graphical building blocks

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Two nodes: or

The basic building blocks of graphs

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X2

X3

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X2

X1 X3

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X1 X2 X3

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X1 X2

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X1 X2

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Immorality

Two nodes: or

The basic building blocks of graphs

Page 69: The Flow of Association and Causation in Graphs

Question:What assumption tells us that X1 and X2are associated, given the following graph?

X1 X2

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Page 70: The Flow of Association and Causation in Graphs

Brady Neal / 35

Chains and forks: dependence

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X1 X2 X3

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The basic building blocks of graphs

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association

The basic building blocks of graphs

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association

The basic building blocks of graphs

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association

association

The basic building blocks of graphs

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association

association

The basic building blocks of graphs

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association

association

The basic building blocks of graphs

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Chains and forks: independence

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association

The basic building blocks of graphs

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X2X1 X3

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Chains and forks: independence

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association

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The basic building blocks of graphs

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Chains and forks: independence

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X2

X1 X3

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association

association

unblocked path unblocked path

The basic building blocks of graphs

Page 79: The Flow of Association and Causation in Graphs

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Proof of conditional independence in chains

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The basic building blocks of graphs

Page 80: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

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<latexit sha1_base64="riJiQia8QKO/6CWE7QcCVOSeD80=">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</latexit>

P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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The basic building blocks of graphs

Page 81: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

The basic building blocks of graphs

Page 82: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

<latexit sha1_base64="QXPWHG5I0Q7gPP2c6TI9cihyOwg=">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</latexit>

1. Bayesian network factorization: P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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The basic building blocks of graphs

Page 83: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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The basic building blocks of graphs

Page 84: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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The basic building blocks of graphs

Page 85: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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The basic building blocks of graphs

Page 86: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

3. Apply Bayes’ rule again:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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The basic building blocks of graphs

Page 87: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

3. Apply Bayes’ rule again:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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P (x1, x3 | x2) =P (x1, x2)

P (x2)P (x3|x2)

= P (x1|x2)P (x3|x2)

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The basic building blocks of graphs

Page 88: The Flow of Association and Causation in Graphs

Brady Neal / 35

Proof of conditional independence in chainsGoal: show

22

X2X1 X3

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P (x1, x3 | x2) = P (x1 | x2)P (x3 | x2)

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1. Bayesian network factorization:

2. Apply Bayes’ rule:

3. Apply Bayes’ rule again:

P (x1, x2, x3) = P (x1)P (x2|x1)P (x3|x2)

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P (x1, x3 | x2) =P (x1)P (x2|x1)P (x3|x2)

P (x2)

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P (x1, x3 | x2) =P (x1, x2)

P (x2)P (x3|x2)

= P (x1|x2)P (x3|x2)

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P (x1, x3 | x2) =P (x1, x2)

P (x2)P (x3|x2)

= P (x1|x2)P (x3|x2)

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The basic building blocks of graphs

Page 89: The Flow of Association and Causation in Graphs

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Proof of conditional independence in forks

Your turn J

23The basic building blocks of graphs

Page 90: The Flow of Association and Causation in Graphs

Brady Neal / 35

Immoralities

24

X1

X2

X3

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The basic building blocks of graphs

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X1 X3

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blocked path

The basic building blocks of graphs

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collider

blocked path

The basic building blocks of graphs

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X1 X3

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collider

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

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blocked path

The basic building blocks of graphs

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X2

X1 X3

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collider

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

blocked path

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

The basic building blocks of graphs

Page 95: The Flow of Association and Causation in Graphs

Brady Neal / 35

Immoralities

24

X2

X1 X3

<latexit sha1_base64="ijsQ+DvJHcj1STrnRi7dE02ScEA=">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</latexit>

collider

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

blocked path

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

The basic building blocks of graphs

Page 96: The Flow of Association and Causation in Graphs

Brady Neal / 35

Immoralities

24

X2

X1 X3

<latexit sha1_base64="ijsQ+DvJHcj1STrnRi7dE02ScEA=">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</latexit>

collider

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

blocked path

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

P (x1, x3) =X

x2

P (x1, x2, x3)

=X

x2

P (x1)P (x3)P (x2 | x1, x3)

= P (x1)P (x3)X

x2

P (x2 | x1, x3)

= P (x1)P (x3)

<latexit sha1_base64="tkR/YMsVXeyJaupzva80mAvAn2U=">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</latexit>

The basic building blocks of graphs

Page 97: The Flow of Association and Causation in Graphs

Brady Neal / 35

Immoralities: conditioning on the collider

25

X2

X1 X3

<latexit sha1_base64="ijsQ+DvJHcj1STrnRi7dE02ScEA=">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</latexit>

blocked path

The basic building blocks of graphs

Page 98: The Flow of Association and Causation in Graphs

Brady Neal / 35

Immoralities: conditioning on the collider

25

X2

X1 X3

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unblocked path

The basic building blocks of graphs

Page 99: The Flow of Association and Causation in Graphs

Brady Neal / 35

Example: good-looking men are jerks

26

X2

X1 X3

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The basic building blocks of graphs

Page 100: The Flow of Association and Causation in Graphs

Brady Neal / 35

Example: good-looking men are jerks

26

X1 =

(1 good-looking

0 otherwise

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X2

X1 X3

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The basic building blocks of graphs

Page 101: The Flow of Association and Causation in Graphs

Brady Neal / 35

Example: good-looking men are jerks

26

X1 =

(1 good-looking

0 otherwise

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X3 =

(1 kind

0 jerk

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X2

X1 X3

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The basic building blocks of graphs

Page 102: The Flow of Association and Causation in Graphs

Brady Neal / 35

Example: good-looking men are jerks

26

X1 =

(1 good-looking

0 otherwise

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X3 =

(1 kind

0 jerk

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X2 = X1 AND X3 =

(1 in relationship

0 not in relationship

<latexit sha1_base64="TZcfoNx3Vt2ujwp6lKqBUSpyuHM=">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</latexit>

X2

X1 X3

<latexit sha1_base64="50asm3XYjk2lTGNJ0xVqCIasUKc=">AAAKdHicfVbvbhs3DHezP229f+32cfugLQnQAhfPdjqkQ2GgQJKiGNqhHdI2Wy4IdDraFqKTBEmXxBHuNfZGe4i9yD6P0l0S+xz3DAMU+SNFUiSlTAtuXb//7521Tz797PO79+53v/jyq6+/efDw2/dWlYbBO6aEMocZtSC4hHeOOwGH2gAtMgEfstPdIP9wBsZyJQ/cTMNxQSeSjzmjDlknD+/8k2Yw4dI7fnqpOXOlgapLmm+T/K5ysNfrVOLyyDrqIBlzIUZMyZwHS1xOtqI3x+TR4fAxoY486if9x8RvHJ4MN6pnt9kI2MFjckQzdQZEwNiRkRqTw+FxVBt8RG37Ws3wyXRRbzvozcWwn0/mY9DUTeuNAQXR3WdL0u1bpXNGd5UcqxKjlxNCDVtl/mgrITm1U8gT4qacnSYkpmlErVWMx1OoE5eQDGRehzPa6SdEKGVBgrWjYW/wSx10E1eKyIUTO3mw3u/140eWiUFDrHea783Jw7t/p7liZQHSMYHOHA362h17ahxnAksgLS1oyk4pxoCkpAXYYx89rcgmcnIyVgb/0tURzWt4Wlg7KzJEFpgO25YF5m2yo9KNnx57LnXpQLJ6o3EpiFMkVC/JuQHmxAwJygxWHiNsSg1lDmu8u7BNVlSLa6VOHc1sFU7vBbouOQOCHAGL/l2M0d4iKnDCSeEWm+QA804a8KJmnZ0FVjik6lrLgnNYMJYo7XjBL5skMlpaKsjEUD21PQT/VtqQVT3b0tQ6CJVjcUvjou1gU/DMUDPzdko12CQHpkwsJptQY9S5TRgVrKF7BTiKDesSrexVu6KfwRCa87Gmtl5ha10ktHQqCb2GKcZukxj9aEBYQSgWZyASCwWvSznqxZb8uWfdTCDUgxBcW8CqN/Q8IQWXvCgLcs5zbIsR1uQO2qhqVa24dNeqxDNusPauVLmUYDBleoSF/AQ3DhOndg3HDtaHsyNfNaYyXpcG5I29kY/xNFHVLdhgQcztebPLULuEUMEnchRGUUKsUBqVuvH8dvFIVIE7FwVmAs9dwnmz8Ol+5dNQzFnm96tqUfaemkZqCh8WLTmXOWhEaDCapD+GH4mLFk5eAaVyCPo4vLaKUwKzhKaNcpib6ISmAosQbpg3yIPGi+BhDuN5yfpgfdgEAcKnRlDtNwJzo0qLUzByWJQ+rKt2eHsKrfJxUYRzixa487mqUhAWGXg4rmGMeUtV05vEId2S5nAjRbolZeqMGhyvYCt/2JI5vCBdGHyVP2iJVOmQRst/Ltmrxz2Omcp/aAkLwHHksPX967Y9NwVzFirgr5UOnlFR+YtVPkapu93NKJut9jTKz1c4G4XFCn+j8LIlBItDC6nKv22fVOXfYJeENgkj0Mb6wRdJHIheq3OsqtDrWwb7yb88eP2q8rvbTwbbL6qV0EyUcIUd7u7s7Q1XYzWO2voWvdLY3wk/9CmNbKx5Pw+6XXcOvPywqbwIVzMO6VkLePUOuMV4iHcOvHTpL4PxJh+07+1l4j0+CZ70fn07XH/+tLnT73W+7/zUedQZdHY6zzsvO2867zpsLVn7Y+1oLb3/X/eH7np3s4au3Wl0vussfN3e/+aroHM=</latexit>

X2 = X1 AND X3 =

(1 in relationship

0 not in relationship

<latexit sha1_base64="TZcfoNx3Vt2ujwp6lKqBUSpyuHM=">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</latexit>

The basic building blocks of graphs

Page 103: The Flow of Association and Causation in Graphs

Brady Neal / 35

Example: good-looking men are jerks

26

X1 =

(1 good-looking

0 otherwise

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X3 =

(1 kind

0 jerk

<latexit sha1_base64="f6URPxn2+0hb77qn/qcxhcPTVPU=">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</latexit>

X2 = X1 AND X3 =

(1 in relationship

0 not in relationship

<latexit sha1_base64="TZcfoNx3Vt2ujwp6lKqBUSpyuHM=">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</latexit>

X2 = X1 AND X3 =

(1 in relationship

0 not in relationship

<latexit sha1_base64="TZcfoNx3Vt2ujwp6lKqBUSpyuHM=">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</latexit>

X2

X1 X3

<latexit sha1_base64="50asm3XYjk2lTGNJ0xVqCIasUKc=">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</latexit>

X2 = X1 AND X3 =

(1 in relationship

0 not in relationship

<latexit sha1_base64="TZcfoNx3Vt2ujwp6lKqBUSpyuHM=">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</latexit>

The basic building blocks of graphs

Page 104: The Flow of Association and Causation in Graphs

Brady Neal / 35

Good-looking men are jerks scatterplot

27The basic building blocks of graphs

looks

kind

ness

Full population

Page 105: The Flow of Association and Causation in Graphs

Brady Neal / 35

Good-looking men are jerks scatterplot

27The basic building blocks of graphs

looks

kind

ness

Groups by availability

Page 106: The Flow of Association and Causation in Graphs

Brady Neal / 35

Good-looking men are jerks scatterplot

27The basic building blocks of graphs

looks

kind

ness

Available men

Page 107: The Flow of Association and Causation in Graphs

Brady Neal / 35

Conditioning on descendants of colliders

28

X1

X2

X3

X4

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The basic building blocks of graphs

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X1 6?? X3 | X4

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The basic building blocks of graphs

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Conditioning on descendants of colliders

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X1 6?? X3 | X4

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The basic building blocks of graphs

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Question:In the three different kinds of three-node graphs, what can block a path?

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Graph terminology

Bayesian networks and causal graphs

The basic building blocks of graphs

The flow of association and causation

The flow of association and causation

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Blocked path definition

31The flow of association and causation

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Blocked path definition

31

A path between nodes X and Y is blocked by a (potentially empty) conditioning set Z if either of the following is true:

The flow of association and causation

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Blocked path definition

31

A path between nodes X and Y is blocked by a (potentially empty) conditioning set Z if either of the following is true:1. Along the path, there is a chain or a fork

where is conditioned on ( ).W

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W 2 Z

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The flow of association and causation

Page 115: The Flow of Association and Causation in Graphs

Brady Neal / 35

Blocked path definition

31

A path between nodes X and Y is blocked by a (potentially empty) conditioning set Z if either of the following is true:1. Along the path, there is a chain or a fork

where is conditioned on ( ).2. There is a collider on the path that is not conditioned on ( )

and none of its descendants are conditioned on ( ).

W

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de(W ) 6✓ Z

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W

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The flow of association and causation

Page 116: The Flow of Association and Causation in Graphs

Brady Neal / 35

Blocked path definition

31

A path between nodes X and Y is blocked by a (potentially empty) conditioning set Z if either of the following is true:1. Along the path, there is a chain or a fork

where is conditioned on ( ).2. There is a collider on the path that is not conditioned on ( )

and none of its descendants are conditioned on ( ).

W

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· · · W ! · · ·

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· · · ! W ! · · ·

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Unblocked path: a path that is not blocked

The flow of association and causation

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d-separation

32The flow of association and causation

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d-separationTwo (sets of) nodes X and Y are d-separated by a set of nodes Z if all of the paths between (any node in) X and (any node in) Y are blocked by Z.

32The flow of association and causation

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X ??G Y | Z =) X ??P Y | Z

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d-separationTwo (sets of) nodes X and Y are d-separated by a set of nodes Z if all of the paths between (any node in) X and (any node in) Y are blocked by Z.

32

Theorem: Given that is Markov with respect to ,P

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G

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The flow of association and causation

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X ??G Y | Z =) X ??P Y | Z

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d-separationTwo (sets of) nodes X and Y are d-separated by a set of nodes Z if all of the paths between (any node in) X and (any node in) Y are blocked by Z.

32

Theorem: Given that is Markov with respect to ,P

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G

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global Markov assumption

The flow of association and causation

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X ??G Y | Z =) X ??P Y | Z

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d-separationTwo (sets of) nodes X and Y are d-separated by a set of nodes Z if all of the paths between (any node in) X and (any node in) Y are blocked by Z.

32

Theorem: Given that is Markov with respect to ,P

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G

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global Markov assumptionlocal Markov assumption ()

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The flow of association and causation

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Brady Neal / 35

X ??G Y | Z =) X ??P Y | Z

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d-separationTwo (sets of) nodes X and Y are d-separated by a set of nodes Z if all of the paths between (any node in) X and (any node in) Y are blocked by Z.

32

Theorem: Given that is Markov with respect to ,P

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G

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global Markov assumptionlocal Markov assumption ()

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Markov assumption

The flow of association and causation

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Brady Neal / 35

d-separation practice

33

T M1 M2 Y

W1

W2

W3

X3

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StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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The flow of association and causation

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StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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association

association

The flow of association and causation

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StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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association

association

The flow of association and causation

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causal association(causation)

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StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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association

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confounding association

causal association(causation)

The flow of association and causation

34

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StatisticalIndependencies

StatisticalDependencies

CausalDependencies

MarkovAssumption

MinimalityAssumption

Causal EdgesAssumption

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causal association

The flow of association and causation

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causal association

non-causal association

non-causal association

The flow of association and causation