Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
Strobl and Thomas A
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The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.
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Causal Learning with the Invariance Principle
Two auxiliary environments suffice to identify causal graphs and functional mechanisms in structural causal models under acyclicity and invariance assumptions, enabling correct counterfactual inference.
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Causal Discovery via Quantile Partial Effect
The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.