Work

Bounds on Causal Effects and Application to High Dimensional Data

Year: 2022

Type: conference-paper

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.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v36i5.20520

Authors: Ang Li, Judea Pearl

Institution: University of California, Los Angeles

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Toyota Research Institute, Toyota Research Institute, North America, Office of Naval Research

Awards: G1088110665, G2535781814, III: Medium: Causal inference in biobanks: Leveraging genetics to infer causal relationships using electronic health records, G8876996369, G8929337198