Convex Mixed-Integer Programming for Invariant Predictions from Multi-environment Data

Abstract

Heterogeneous data provide an opportunity to identify causal predictors by revealing which predictive relationships remain invariant. Under a linear model, we introduce a least-squares coefficient-and-risk (LSCR) invariance principle, requiring both ordinary least squares coefficients and prediction risks to remain constant across environments. Our model neither specifies an intervention mechanism nor assumes a structural causal model for the entire system. We develop an invariance score that quantifies departures from LSCR invariance and use its optimization to construct two complementary procedures for causal predictor identification. First, we propose an estimator that jointly minimizes prediction risk and invariance penalty, and establish non-asymptotic guarantees for estimation and recovery of the causal parents under suitable identifiability conditions. Second, without requiring full identifiability, we develop a testing procedure that minimizes the invariance score over all predictor sets excluding a given variable, enabling valid causal discoveries with asymptotic error control via a multiplier bootstrap. Both procedures admit convex mixed-integer optimization formulations that avoid exhaustive subset enumeration while providing certificates of optimality. Simulations and real data experiments demonstrate substantial statistical and computational improvements over existing methods.