Structural equation modeling for causal effect estimation with machine learning

Causal inference is a crucial framework in a variety of fields, such as economics, healthcare, and social science. In this context, data-driven machine learning models have become more popular for estimating the effects of treatments. One fundamental technique for causal inference is structural equation modeling (SEM), which makes it possible to estimate the relationships between variables. However, conventional SEM methods need help with the complexities of structural causal models (SCM) in real-world data, including latent confounders, nonlinear relationships, and challenges in accurately specifying model structures. These limitations could cause the interpretation or biased causal effects. To address these challenges, we introduce a new proposed method combining the piecewise structural equation modeling (PSEM) with the backdoor criterion, named PSEMBC. The main innovation of PSEMBC is the estimation of causal effects with the use of a linear SCM model, which captures complex relationships and interactions in the data. We demonstrate the value of PSEMBC for precisely and reliably identifying the average treatment effect (ATE) in simulated and real-world datasets utilizing a comparative study with current causal inference approaches. © 2025 Elsevier B.V., All rights reserved.