Talk: Joint causal inference on observational and experimental data - NIPS 2016 "What If?" workshop...

Joint causal inferenceon observational and experimental datasets

Sara Magliacane, Tom Claassen, Joris M. Mooij

[email protected]

What If?, 10th December, 2016

Sara Magliacane (VU, UvA) Joint causal inference 10-12-2016 1 / 21

[email protected]

Part I

Introduction


Causal inference: learning causal relations from data

Definition

X causes Y (X 99KY ) = intervening upon (changing) X changes Y

We can represent causal relations with a causal DAG (hidden vars):

X Y

Z

E.g. X = Smoking, Y = Cancer, Z = Genetics

Causal inference = structure learning of the causal DAG

Traditionally, causal relations are inferred from interventions.

Sometimes, interventions are unethical, unfeasible or too expensive



Definition



X Y

Z







Definition



X Y

Z







Definition



X Y

Z






Causal inference from observational and experimental data

Holy Grail of Causal Inference

Learn as much causal structure as possible from observations,integrating background knowledge and experimental data.

Current causal inference methods:

Score-based: evaluate models using a penalized likelihood score

Constraint-based: use statistical independences to expressconstraints over possible causal models

Advantage of constraint-based methods:

can handle latent confounders naturally




















Joint inference on observational and experimental data

Advantage of score-based methods:

can formulate joint inference on observational and experimental dataand learn the targets of interventions, e.g. [Eaton and Murphy, 2007].

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Figure 6: Models of the biological data. (a) A partial model of the T-cell pathway, as currently accepted by biologists. The small roundcircles with numbers represent various interventions (green = activators, red = inhibitors). From [SPP+05]. Reprinted with permissionfrom AAAS. (b) Edges with marginal probability above 0.5 as estimated by [SPP+05]. (c) Edges with marginal probability above 0.5as estimated by us, assuming known perfect interventions. Dashed edges are ones that are missing from the union of (a) and (b). Theseare either false positives, or edges that Sachs et al missed. (d) Edges with marginal probability above 0.5 as estimated by us, assuminguncertain, imperfect interventions, and a fan-in bound of k = 2. The intervention nodes are in red, and edges from the interventionnodes are light gray. Dashed edges are ones that are missing from the union of (a) and (b). This figure is best viewed in colour.

Example from [Eaton and Murphy, 2007]

Goal: Can we perform joint inference using constraint-based methods?


Joint inference on observational and experimental data

Advantage of score-based methods:

can formulate joint inference on observational and experimental dataand learn the targets of interventions, e.g. [Eaton and Murphy, 2007].

raf

mek12

pip2

erk

akt

p38jnk

pkc

plcy

pip3

pka

(a) (b)

raf

plcy

pip2

pip3

erk

p38

pkc

akt

pka

mek12

PresentMissing jnk

B2cAMP

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akt

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Psitect AKT inh U0126PMA

p38

G06967mek12

raf

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(c) (d)

Figure 6: Models of the biological data. (a) A partial model of the T-cell pathway, as currently accepted by biologists. The small roundcircles with numbers represent various interventions (green = activators, red = inhibitors). From [SPP+05]. Reprinted with permissionfrom AAAS. (b) Edges with marginal probability above 0.5 as estimated by [SPP+05]. (c) Edges with marginal probability above 0.5as estimated by us, assuming known perfect interventions. Dashed edges are ones that are missing from the union of (a) and (b). Theseare either false positives, or edges that Sachs et al missed. (d) Edges with marginal probability above 0.5 as estimated by us, assuminguncertain, imperfect interventions, and a fan-in bound of k = 2. The intervention nodes are in red, and edges from the interventionnodes are light gray. Dashed edges are ones that are missing from the union of (a) and (b). This figure is best viewed in colour.

Example from [Eaton and Murphy, 2007]

Goal: Can we perform joint inference using constraint-based methods?


Part II

Joint Causal Inference


Joint Causal Inference: Assumptions

Idea: Model jointly several observational or experimental datasets{Dr}r∈{1...n} with zero or more possibly unknown intervention targets.

We assume a unique underlying causal DAG across datasets definedover system variables {Xj}j∈X (some of which possibly hidden).

Example

X1 X2

X3

Dataset D1

unknown ints

X1 X2

X3

Dataset D2

unknown ints

X1 X2

X3

Dataset D3

do X1

Note: cannot handle certain intervention types, e.g. perfect interventions.





Example

X1 X2

X3

Dataset D1

unknown ints

X1 X2

X3

Dataset D2

unknown ints

X1 X2

X3

Dataset D3

do X1






Example

X1 X2

X3

Dataset D1

unknown ints

X1 X2

X3

Dataset D2

unknown ints

X1 X2

X3

Dataset D3

do X1



Joint Causal Inference: SCM

We introduce two types of dummy variables in the data:

a regime variable R, indicating which dataset Dr a data point isfrom

intervention variables {Ii}i∈I , functions of R

We assume that we can represent the whole system as an acyclic SCM:

R = ER ,

Ii = gi (R), i ∈ I,Xj = fj(Xpa(Xj )∩X , Ipa(Xj )∩I ,Ej), j ∈ X ,

P((Ek)k∈X∪{R}

)=

∏k∈X∪{R}

P(Ek).


Joint Causal Inference: SCM

We introduce two types of dummy variables in the data:

a regime variable R, indicating which dataset Dr a data point isfrom

intervention variables {Ii}i∈I , functions of R

We assume that we can represent the whole system as an acyclic SCM:

R = ER ,

Ii = gi (R), i ∈ I,Xj = fj(Xpa(Xj )∩X , Ipa(Xj )∩I ,Ej), j ∈ X ,

P((Ek)k∈X∪{R}

)=

∏k∈X∪{R}

P(Ek).


Joint Causal Inference: single joint causal DAG

We represent the SCM with a causal DAG C representing all datasetsjointly:

R I1 I2 X1 X2 X4

1 20 0 0.1 0.2 0.51 20 0 0.13 0.21 0.491 20 0 . . . . . . . . .

2 20 1 . . . . . . . . .

3 30 0 . . . . . . . . .

4 30 1 . . . . . . . . .

4 datasets with 2 interventions

R

I1 I2

X1

X2

X3

X4

Causal DAG C

We assume Causal Markov and Minimality hold in C.


Joint Causal Inference: Faithfulness violations

Causal Faithfulness assumption:

X ⊥⊥ Y |W =⇒ X ⊥d Y |W [C]

R I1 X1 I1 = g1(R) =⇒ X1 ⊥⊥ I1 |R ... but X1 6⊥d I1 |R

Solution: D-separation [Spirtes et al., 2000]: X1 ⊥D I1 |R

Problem: ⊥D provably complete (for now) for functionally determinedrelations

Solution: restrict deterministic relations to only ∀i ∈ I : Ii = gi (R)

D-Faithfulness: X ⊥⊥ Y |W =⇒ X ⊥D Y |W [C].




X ⊥⊥ Y |W =⇒ X ⊥d Y |W [C]









X ⊥⊥ Y |W =⇒ X ⊥d Y |W [C]









X ⊥⊥ Y |W =⇒ X ⊥d Y |W [C]








Joint Causal Inference (JCI) = Given all the assumptions, reconstructthe causal DAG C from independence test results.

X1 6⊥⊥ I1

X1 ⊥⊥ I1 |R

X4 ⊥⊥ X2 | I1. . .

=⇒

R

I1 I2

X1

X2

X3

X4

Problem: Current constraint-based methods cannot work with JCI,because of faithfulness violations.




X1 6⊥⊥ I1

X1 ⊥⊥ I1 |R

X4 ⊥⊥ X2 | I1. . .

=⇒

R

I1 I2

X1

X2

X3

X4





X1 6⊥⊥ I1

X1 ⊥⊥ I1 |R

X4 ⊥⊥ X2 | I1. . .

=⇒

R

I1 I2

X1

X2

X3

X4





X1 6⊥⊥ I1

X1 ⊥⊥ I1 |R

X4 ⊥⊥ X2 | I1. . .

=⇒

R

I1 I2

X1

X2

X3

X4



Part III

Extending constraint-based methods for JCI


A simple strategy for dealing with faithfulness violations

Idea: Faithfulness violations → Partial inputs

A simple strategy for dealing with functionally determined relations:

1 Rephrase constraint-based method in terms of d-separations

2 For each independence test result derive sound d-separations:

X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W

X 6∈ Det(W ) and Y 6∈ Det(W ) and X ⊥⊥ Y |W =⇒X ⊥d Y |Det(W )

where Det(W ) = variables determined by (a subset of) W .

Conjecture: sound also for a larger class of deterministic relations.







X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W










X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W










X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W










X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W










X 6⊥⊥ Y |W =⇒ X 6⊥d Y |W





Ancestral Causal Inference with Determinism (ACID)


Traditional constraint-based methods (e.g., PC, FCI, ...):

cannot handle partial inputs

cannot exploit the rich background knowledge in JCI

Solution: Logic-based methods, e.g., ACI [Magliacane et al., 2016]

=⇒ we implement the strategy in an extension of ACI called:


























Rephrasing ACI rules in terms of d-separations

ACI rules:

Example

For X , Y , W disjoint (sets of) variables:

(X ⊥⊥Y |W ) ∧ (X 699KW ) =⇒ X 699KY

ACID rules:

Example


(X ⊥d Y |W ) ∧ (X 699KW ) =⇒ X 699KY


Rephrasing ACI rules in terms of d-separations

ACI rules:

Example


(X ⊥⊥Y |W ) ∧ (X 699KW ) =⇒ X 699KY

ACID rules:

Example


(X ⊥d Y |W ) ∧ (X 699KW ) =⇒ X 699KY


ACID-JCI

ACID-JCI = ACID rules + sound d-separations from strategy

+ JCI background knowledge

For example:

∀i ∈ I,∀j ∈ X : (Xj 699KR) ∧ (Xj 699K Ii )

“Standard variables cannot cause dummy variables”

Example

I1

X1

R

I1

X1

R

I1 X1

R


ACID-JCI



For example:

∀i ∈ I, ∀j ∈ X : (Xj 699KR) ∧ (Xj 699K Ii )


Example

I1

X1

R

I1

X1

R

I1 X1

R


ACID-JCI



For example:

∀i ∈ I, ∀j ∈ X : (Xj 699KR) ∧ (Xj 699K Ii )


Example

I1

X1

R

I1

X1

R

I1 X1

R


Part IV

Evaluation


Simulated data accuracy: example Precision Recall curve

Ancestral (“causes”) relations Non-ancestral (“not causes”)

Precision Recall curves of 2000 randomly generated causal graphs for4 system variables and 3 interventions

ACID-JCI substantially improves accuracy w.r.t. merging learntstructures (merged ACI)


Conclusion

Joint Causal Inference, a powerful formulation of causal discoveryover multiple datasets for constraint-based methods

A simple strategy for dealing with faithfulness violations due tofunctionally determined relations

An implementation, ACID-JCI, that substantially improves theaccuracy w.r.t. state-of-the-art

Future work:

Improve scalability (now max 7 variables in C).

Working paper: https://arxiv.org/abs/1611.10351

Collaborators:

Tom Claassen Joris M. Mooij


https://arxiv.org/abs/1611.10351

Conclusion




Future work:



Collaborators:




Conclusion




Future work:



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Conclusion




Future work:



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Conclusion




Future work:



Collaborators:




References I

Claassen, T. and Heskes, T. (2011).

A logical characterization of constraint-based causal discovery.In UAI 2011, pages 135–144.

Eaton, D. and Murphy, K. (2007).

Exact Bayesian structure learning from uncertain interventions.In Proceedings of the 10th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 107–114.

Magliacane, S., Claassen, T., and Mooij, J. M. (2016).

Ancestral causal inference.In NIPS.

Mooij, J. M. and Heskes, T. (2013).

Cyclic causal discovery from continuous equilibrium data.In Nicholson, A. and Smyth, P., editors, Proceedings of the 29th Annual Conference on Uncertainty in ArtificialIntelligence (UAI-13), pages 431–439. AUAI Press.

Spirtes, P., Glymour, C., and Scheines, R. (2000).

Causation, Prediction, and Search.MIT press, 2nd edition.


Ancestral Causal Inference (ACI)

Weighted list of inputs: I = {(ij ,wj)}:

E.g. I = { (Y ⊥⊥Z |X , 0.2), (Y 6⊥⊥X , 0.1)} }

Any consistent weighting scheme, e.g. frequentist, Bayesian

For any possible ancestral structure C , we define the loss function:

Loss(C , I ) :=∑

(ij ,wj )∈I : ij is not satisfied in C

wj

Here: “ij is not satisfied in C” = defined by ancestral reasoning rules

For each possible causal relation X 99KY provide score:

Conf (X 99KY ) = minC∈CLoss(C , I + (X 699KY ,∞))

−minC∈CLoss(C , I + (X 99KY , ∞))


Date post:	14-Feb-2017
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Talk: Joint causal inference on observational and experimental data - NIPS 2016 "What If?" workshop...

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