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Post-selection inference for causal effects after causal discovery
Algorithms for constraint-based causal discovery select graphical causal models among a
space of possible candidates (eg, all directed acyclic graphs) by executing a sequence of …
space of possible candidates (eg, all directed acyclic graphs) by executing a sequence of …
Linear deconfounded score method: scoring DAGs with dense unobserved confounding
This article deals with the discovery of causal relations from a combination of observational
data and qualitative assumptions about the nature of causality in the presence of …
data and qualitative assumptions about the nature of causality in the presence of …
Consistency of Neural Causal Partial Identification
Abstract Recent progress in Neural Causal Models (NCMs) showcased how identification
and partial identification of causal effects can be automatically carried out via training of …
and partial identification of causal effects can be automatically carried out via training of …
Your Assumed DAG is Wrong and Here's How To Deal With It
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal
relationships between variables is a common starting point for cause-effect estimation …
relationships between variables is a common starting point for cause-effect estimation …
Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
As many practical fields transition to provide personalized decisions, data is increasingly
relevant to support the evaluation of candidate plans and policies (eg, guidelines for the …
relevant to support the evaluation of candidate plans and policies (eg, guidelines for the …