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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Cynthia Rudin
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in othe…
Interpretable Almost Matching Exactly for Causal Inference
Yameng Liu, Aw Dieng, Sudeepa Roy +2
We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social…
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Beau Coker, Cynthia Rudin, Gary King
Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along w…
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
Siong Thye Goh, Cynthia Rudin
We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. To overcome the problem th…
Direct Learning to Rank and Rerank
Cynthia Rudin, Yining Wang
Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited…