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Found 4 papers, 1 papers with code
Non-parametric identifiability and sensitivity analysis of synthetic control models
While identifiability of the causal estimand in such models has been obtained from a range of assumptions, it is widely and implicitly assumed that the underlying assumptions are satisfied for all time periods both pre- and post-intervention.
The Causal Marginal Polytope for Bounding Treatment Effects
Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.
Stochastic Causal Programming for Bounding Treatment Effects
Causal effect estimation is important for many tasks in the natural and social sciences.
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.
