Bayesian sample size determination for causal discovery

TL;DR

This paper introduces a Bayesian approach to determine the optimal sample size for interventional experiments in causal discovery, improving the efficiency and accuracy of identifying causal DAGs from observational and interventional data.

Contribution

It proposes a novel Bayesian experimental design method to calculate the necessary sample size for interventions in causal graph learning, addressing a key gap in existing algorithms.

Findings

Method effectively estimates sample sizes for interventions.

Enhances causal discovery accuracy with fewer resources.

Provides a pre-experimental evaluation framework.

Abstract

Graphical models based on Directed Acyclic Graphs (DAGs) are widely used to answer causal questions across a variety of scientific and social disciplines. However, observational data alone cannot distinguish in general between DAGs representing the same conditional independence assertions (Markov equivalent DAGs); as a consequence the orientation of some edges in the graph remains indeterminate. Interventional data, produced by exogenous manipulations of variables in the network, enhance the process of structure learning because they allow to distinguish among equivalent DAGs, thus sharpening causal inference. Starting from an equivalence class of DAGs, a few procedures have been devised to produce a collection of variables to be manipulated in order to identify a causal DAG. Yet, these algorithmic approaches do not determine the sample size of the interventional data required to obtain a desired level of statistical accuracy. We tackle this problem from a Bayesian experimental design perspective, taking as input a sequence of target variables to be manipulated to identify edge orientation. We then propose a method to determine, at each intervention, the optimal sample size capable of producing a successful experiment based on a pre-experimental evaluation of the overall probability of substantial correct evidence.