Causal inference under interference: computational barriers and algorithmic solutions

TL;DR

This paper investigates the computational challenges of estimating causal effects under interference in network data, proposing algorithms for specific graph structures and analyzing their theoretical properties.

Contribution

It demonstrates the computational hardness of treatment effect evaluation without assumptions and introduces polynomial-time algorithms for dense and Gaussian interaction models.

Findings

Polynomial time evaluation is hard without assumptions.

Algorithms work efficiently for dense and Gaussian graphs.

Estimates are stable and provably approximate in polynomial time.

Abstract

We study causal effect estimation under interference from network data. We work under the chain-graph formulation pioneered in Tchetgen Tchetgen et. al (2021). Our first result shows that polynomial time evaluation of treatment effects is computationally hard in this framework without additional assumptions on the underlying chain graph. Subsequently, we assume that the interactions among the study units are governed either by (i) a dense graph or (ii) an i.i.d. Gaussian matrix. In each case, we show that the treatment effects have well-defined limits as the population size diverges to infinity. Additionally, we develop polynomial time algorithms to consistently evaluate the treatment effects in each case. Finally, we estimate the unknown parameters from the observed data using maximum pseudo-likelihood estimates, and establish the stability of our causal effect estimators under this perturbation. Our algorithms provably approximate the causal effects in polynomial time even in low-temperature regimes where the canonical MCMC samplers are slow mixing. For dense graphs, our results use the notion of regularity partitions; for Gaussian interactions, our approach uses ideas from spin glass theory and Approximate Message Passing.