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Found 6 papers, 2 papers with code
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters
Recently, we have seen an acceleration of research related to CDM and causal effect estimation (CEE) using machine-learned models.
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation
This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attention from economists, computer scientists, and social scientists.
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
We examine counterfactual explanations for explaining the decisions made by model-based AI systems.
Counterfactual Explanation Algorithms for Behavioral and Textual Data
This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets.
Explaining Classification Models Built on High-Dimensional Sparse Data
We evaluate these alternatives in terms of explanation "bang for the buck,", i. e., how many examples' inferences are explained for a given number of features listed.
Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals
Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy.
