A Uniformly Consistent Estimator of Causal Effects under the $k$-Triangle-Faithfulness Assumption

TL;DR

This paper introduces a new uniformly consistent estimator for causal effects that operates under the weaker k-Triangle-Faithfulness assumption, broadening applicability beyond previous assumptions.

Contribution

It presents the first estimator that is uniformly consistent under the weaker k-Triangle-Faithfulness assumption, extending causal inference methods.

Findings

Estimator is uniformly consistent under k-Triangle-Faithfulness.

Works for linear Gaussian causal structures.

Estimates both Markov equivalence class and structural coefficients.

Abstract

Spirtes, Glymour and Scheines [Causation, Prediction, and Search (1993) Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 (2003) 491-515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and B\"{u}hlmann [J. Mach. Learn. Res. 8 (2007) 613-636] described a uniformly consistent estimator of the Markov equivalence class of a linear Gaussian causal structure under the Causal Markov and Strong Causal Faithfulness assumptions. However, the Strong Faithfulness assumption may be false with high probability in many domains. We describe a uniformly consistent estimator of both the Markov equivalence class of a linear Gaussian causal structure and the identifiable structural coefficients in the Markov equivalence class under the Causal Markov assumption and the considerably weaker k-Triangle-Faithfulness assumption.