Surrogate Outcomes and Transportability

Abstract Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments [1] and we label it as surrogate outcome identifiability. We show that the concept of transportability [2] provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.

[1]  Judea Pearl,et al.  Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models , 2006, AAAI.

[2]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[3]  Franz von Kutschera,et al.  Causation , 1993, J. Philos. Log..

[4]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[5]  Santtu Tikka,et al.  Identifying Causal Effects with the R Package causaleffect , 2017, 1806.07161.

[6]  Elias Bareinboim,et al.  A General Algorithm for Deciding Transportability of Experimental Results , 2013, ArXiv.

[7]  J. Pearl,et al.  Studies in causal reasoning and learning , 2002 .

[8]  Judea Pearl,et al.  Complete Identification Methods for the Causal Hierarchy , 2008, J. Mach. Learn. Res..

[9]  Elias Bareinboim,et al.  External Validity: From Do-Calculus to Transportability Across Populations , 2014, Probabilistic and Causal Inference.

[10]  Elias Bareinboim,et al.  Transportability from Multiple Environments with Limited Experiments: Completeness Results , 2014, NIPS.

[11]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[13]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[14]  Marco Valtorta,et al.  Pearl's Calculus of Intervention Is Complete , 2006, UAI.

[15]  Elias Bareinboim,et al.  Meta-Transportability of Causal Effects: A Formal Approach , 2013, AISTATS.

[16]  Jin Tian,et al.  Recovering Causal Effects from Selection Bias , 2015, AAAI.

[17]  Elias Bareinboim,et al.  Causal Inference by Surrogate Experiments: z-Identifiability , 2012, UAI.