Bounds on Causal Effects and Application to High Dimensional Data
Ang Li, Judea Pearl
[AAAI-22] Main Track
Abstract:
This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed. For such scenarios, we derive bounds on the causal effects by solving two non-linear optimization problems, and demonstrate that the bounds are sufficient. Using this optimization method, we propose a framework for dimensionality reduction that allows one to trade bias for estimation power, and demonstrate its performance using simulation studies.
Introduction Video
Sessions where this paper appears
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Poster Session 4
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Poster Session 8
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