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Found 4 papers, 1 papers with code
Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification
We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC).
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.
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