Titlebook: Causal Inference; 6th Pacific Causal I Xiao-Hua Zhou,Jinzhu Jia Conference proceedings 2025 The Editor(s) (if applicable) and The Author(s)

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,Avoiding the Unconfoundednes Assumption: Counterfactual Inference Considering Unobserved Confounderof Unconfoundedness. Furthermore, within the framework, we incorporate a balanced representation model that takes into account both observed and unobserved confounders. This integration guarantees the attainment of unbiased inferences. The CIUC framework is versatile, as it can accommodate both disc

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Exploring the Use of Q-Learning in Causal Inference for Adaptive Interventions,ty of populations; adaptive interventions that are effective in one subgroup may be ineffective in another. Response-adaptive designs potentially reducing the likelihood of selecting the most appropriate treatment for each individual..In this context, we present Q-learning, an extension of regressio

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D. Jeanmonod,M. Sindou,F. Mauguièreof Unconfoundedness. Furthermore, within the framework, we incorporate a balanced representation model that takes into account both observed and unobserved confounders. This integration guarantees the attainment of unbiased inferences. The CIUC framework is versatile, as it can accommodate both disc

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J. I. Ruz-Franzi,J. M. González-Dardervery level of parental SES, the attempt would nonetheless fail to eliminate the marginal Black-White gap in schooling due to the endogeneity of parental SES. We propose a novel solution that replaces the original scale of an endogenous confounder with a scale preserving one’s within-group relative s

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M. Dolores Ruiz-Medina,Mariano J. Valderramaty of populations; adaptive interventions that are effective in one subgroup may be ineffective in another. Response-adaptive designs potentially reducing the likelihood of selecting the most appropriate treatment for each individual..In this context, we present Q-learning, an extension of regressio