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Poster
Experimental Design for Learning Causal Graphs with Latent Variables
Murat Kocaoglu · Karthikeyan Shanmugam · Elias Bareinboim

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #184 #None
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We consider the problem of learning causal structures with latent variables using interventions. Our objective is not only to learn the causal graph between the observed variables, but to locate unobserved variables that could confound the relationship between observables. Our approach is stage-wise: We first learn the observable graph, i.e., the induced graph between observable variables. Next we learn the existence and location of the latent variables given the observable graph. We propose an efficient randomized algorithm that can learn the observable graph using O(d\log^2 n) interventions where d is the degree of the graph. We further propose an efficient deterministic variant which uses O(log n + l) interventions, where l is the longest directed path in the graph. Next, we propose an algorithm that uses only O(d^2 log n) interventions that can learn the latents between both non-adjacent and adjacent variables. While a naive baseline approach would require O(n^2) interventions, our combined algorithm can learn the causal graph with latents using O(d log^2 n + d^2 log (n)) interventions.

Keywords: Causal Inference

Author Information

Murat Kocaoglu (MIT-IBM Watson AI Lab)
Karthikeyan Shanmugam (IBM Research, NY)
Elias Bareinboim (Purdue)

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