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 equa...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 475; S. 117020
Hauptverfasser: Ahmad, Sohail, Shah, Kamal, Debbouche, Amar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.03.2026
Schlagworte:
Backdoor criterion
Causal inference
Confounding variables
Machine learning
Piecewise SEM
Structural causal model
Causal inference
Confounding variables
Piecewise SEM
Structural causal model
Backdoor criterion
Machine learning
ISSN:0377-0427
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Zusammenfassung: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.
ISSN:0377-0427
DOI:10.1016/j.cam.2025.117020