S-DIDML restates double-machine-learning residualization followed by group-time fixed effects regression, without formal validation or evidence that it avoids known two-way fixed effects pitfalls.
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Bridging Structural Causal Inference and Machine Learning The S-DIDML Estimator for Heterogeneous Treatment Effects
S-DIDML restates double-machine-learning residualization followed by group-time fixed effects regression, without formal validation or evidence that it avoids known two-way fixed effects pitfalls.