Identifying Conditional Causal Effects

TL;DR

This paper presents a polynomial-time method for identifying conditional causal effects from nonexperimental data using causal graphs with unobserved variables, expressing effects in terms of observed distributions.

Contribution

It introduces a systematic procedure for identifying conditional causal effects in complex causal graphs with unobserved variables, expanding causal inference capabilities.

Findings

Provides a polynomial-time identification procedure

Expresses effects in terms of observed joint distributions

Handles causal graphs with unobserved variables

Abstract

This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution.