A Uniformly Consistent Estimator of non-Gaussian Causal Effects Under the k-Triangle-Faithfulness Assumption

TL;DR

This paper introduces a new assumption called Generalized k-Triangle Faithfulness that extends causal effect estimation to non-Gaussian distributions, providing consistent algorithms for causal inference.

Contribution

It proposes the Generalized k-Triangle Faithfulness assumption and develops algorithms for uniformly consistent causal effect estimation beyond Gaussian models.

Findings

The Edge Estimation Algorithm achieves consistent causal effect estimates under the new assumption.

The Very Conservative SGS Algorithm consistently estimates the Markov equivalence class.

The approach applies to any smooth distribution, broadening causal inference applicability.

Abstract

Kalisch and B\"{u}hlmann (2007) showed that for linear Gaussian models, under the Causal Markov Assumption, the Strong Causal Faithfulness Assumption, and the assumption of causal sufficiency, the PC algorithm is a uniformly consistent estimator of the Markov Equivalence Class of the true causal DAG for linear Gaussian models; it follows from this that for the identifiable causal effects in the Markov Equivalence Class, there are uniformly consistent estimators of causal effects as well. The $k$-Triangle-Faithfulness Assumption is a strictly weaker assumption that avoids some implausible implications of the Strong Causal Faithfulness Assumption and also allows for uniformly consistent estimates of Markov Equivalence Classes (in a weakened sense), and of identifiable causal effects. However, both of these assumptions are restricted to linear Gaussian models. We propose the Generalized $k$-Triangle Faithfulness, which can be applied to any smooth distribution. In addition, under the Generalized $k$-Triangle Faithfulness Assumption, we describe the Edge Estimation Algorithm that provides uniformly consistent estimates of causal effects in some cases (and otherwise outputs "can't tell"), and the \textit{Very Conservative }$SGS$ Algorithm that (in a slightly weaker sense) is a uniformly consistent estimator of the Markov equivalence class of the true DAG.