Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
This work addresses the problem of causal inference in social networks for researchers and practitioners, offering a robust and scalable solution that reduces reliance on strong assumptions, though it is incremental in building on existing frameworks.
The paper tackles the challenge of inferring causal effects in social networks by addressing interference and network-induced confounding, proposing a novel method that combines graph machine learning with double machine learning to accurately estimate direct and peer effects, as demonstrated through simulations and an application to financial risk tolerance.
We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is extensive literature focusing on estimating causal effects in social network setups, a majority of them make prior assumptions about the form of network-induced confounding mechanisms. Such strong assumptions are rarely likely to hold especially in high-dimensional networks. We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework to enable accurate and efficient estimation of direct and peer effects using a single observational social network. We demonstrate the semiparametric efficiency of our proposed estimator under mild regularity conditions, allowing for consistent uncertainty quantification. We demonstrate that our method is accurate, robust, and scalable via an extensive simulation study. We use our method to investigate the impact of Self-Help Group participation on financial risk tolerance.