Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models

Abstract

Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our study explores how to learn joint interventional effects using only observational data and single-variable interventions. We present an identifiability result for this problem, showing that for a class of nonlinear additive outcome mechanisms, joint effects can be inferred without access to joint interventional data. We propose a practical estimator that decomposes the causal effect into confounded and unconfounded contributions for each intervention variable. Experiments on synthetic data demonstrate that our method achieves performance comparable to models trained directly on joint interventional data, outperforming a purely observational estimator.

Publication
International Conference on Machine Learning
Armin Kekić
Armin Kekić
PhD Student in Causality and Machine Learning

My interests include high-dimensional time series forecasting, machine learning, network science and quantum dynamics.