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Found 10 papers, 8 papers with code
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.
Falsification before Extrapolation in Causal Effect Estimation
Under the assumption that at least one observational estimator is asymptotically normal and consistent for both the validation and extrapolated effects, we provide guarantees on the coverage probability of the intervals output by our algorithm.
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance.
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Individuals often make different decisions when faced with the same context, due to personal preferences and background.
Regularizing towards Causal Invariance: Linear Models with Proxies
In the case of two proxy variables, we propose a modified estimator that is prediction optimal under interventions up to a known strength.
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
Reinforcement learning (RL) has the potential to significantly improve clinical decision making.
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options.
ML4H Abstract Track 2019
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.
Characterization of Overlap in Observational Studies
Overlap between treatment groups is required for non-parametric estimation of causal effects.
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
We introduce an off-policy evaluation procedure for highlighting episodes where applying a reinforcement learned (RL) policy is likely to have produced a substantially different outcome than the observed policy.
