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Found 5 papers, 2 papers with code
Sparse and Faithful Explanations Without Sparse Models
In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied.
A Double Machine Learning Approach to Combining Experimental and Observational Data
Experimental and observational studies often lack validity due to untestable assumptions.
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates.
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space.
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes.
