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Found 3 papers, 2 papers with code
Local Causal Discovery for Estimating Causal Effects
Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables.
Estimating Treatment Effects with Observed Confounders and Mediators
We show that it strictly outperforms the backdoor and frontdoor estimators and that this improvement can be unbounded.
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