Adaptive Online Experimental Design for Causal Discovery

TL;DR

This paper introduces an adaptive online experimental design algorithm for causal discovery that efficiently identifies causal graphs with fewer interventions by actively selecting interventions based on sampling history.

Contribution

It formalizes causal discovery as an online learning problem and proposes a track-and-stop algorithm that adaptively chooses interventions to minimize sampling while ensuring confidence.

Findings

The algorithm outperforms existing methods in simulations.

It achieves higher accuracy with fewer samples.

Provides a problem-dependent upper bound on sample complexity.

Abstract

Causal discovery aims to uncover cause-and-effect relationships encoded in causal graphs by leveraging observational, interventional data, or their combination. The majority of existing causal discovery methods are developed assuming infinite interventional data. We focus on data interventional efficiency and formalize causal discovery from the perspective of online learning, inspired by pure exploration in bandit problems. A graph separating system, consisting of interventions that cut every edge of the graph at least once, is sufficient for learning causal graphs when infinite interventional data is available, even in the worst case. We propose a track-and-stop causal discovery algorithm that adaptively selects interventions from the graph separating system via allocation matching and learns the causal graph based on sampling history. Given any desired confidence value, the algorithm determines a termination condition and runs until it is met. We analyze the algorithm to establish a problem-dependent upper bound on the expected number of required interventional samples. Our proposed algorithm outperforms existing methods in simulations across various randomly generated causal graphs. It achieves higher accuracy, measured by the structural hamming distance (SHD) between the learned causal graph and the ground truth, with significantly fewer samples.