Bounds on Causal Effects and Application to High Dimensional Data

No. 5: AAAI-22 Technical Tracks 5

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Technical Track on Knowledge Representation and Reasoning

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.

10.1609/aaai.v36i5.20520