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Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Content Provider	Scilit
Author	Malinsky, Daniel Spirtes, Peter
Copyright Year	2017
Description	Journal: International Journal of Approximate Reasoning We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent confounders. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to adjust for) to estimate a set of possible causal effects. Our approach is based on the IDA procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no latent confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm in simulation experiments.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711475/pdf
Ending Page	384
Page Count	14
Starting Page	371
ISSN	0888613X
DOI	10.1016/j.ijar.2017.06.005
Journal	International Journal of Approximate Reasoning
Volume Number	88
Language	English
Publisher	Elsevier BV
Publisher Date	2017-09-01
Access Restriction	Open
Subject Keyword	Journal: International Journal of Approximate Reasoning Statistics and Probability Causal Inference Markov Equivalence Ancestral Graphs Latent Confounding
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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