Learning Cost-Effective and Interpretable Treatment Regimes

By Himabindu Lakkaraju, Cynthia Rudin. 2017

102 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Medical & Health AI

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Citations in the corpus

Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, background): “Algorithmic risk assessments are increasingly used to make and inform decisions in a variety of high-stakes settings ranging from health care, the child welfare and criminal justice systems to consumer lending and hiring [1, 2, 3, 4, 5, 6, 7, 8, 9].”
Learning customized and optimized lists of rules with mathematical programming (2018, neutral): “Some recent work Zhang et al (2015); Lakkaraju and Rudin (2017) has used the form of rule lists to create optimal treatment regimes, which the MIP framework provided here could be adapted to handle.”
Boolean Decision Rules via Column Generation (2018, background): “Rule sets stand in contrast to decision lists [44, 35, 49, 3, 34, 53], where rules are ordered in an IF-ELSE sequence, and decision trees [11, 43, 6], where they are organized into a tree structure.”
Learning Credible Models (2017, background): “The most common forms of regularization, l 1 (LASSO) and l 2 θ represents the model parameters; β ∈ [ 0 , 1 ] is a hyperparameter that controls the tradeoff between the l 1 and l 2 norms; w is a set of non-negative weights for each feature; w ∗ | θ | [ i ] is the i th largest parameter sorted by…”
Inverse Classification for Comparison-based Interpretability in Machine Learning (2017, methodology): “Other approaches consist in building a new simpler model to replace the original classiﬁer (Lakkaraju and Rudin 2017; Angelino et al. 2017).”
Fair Decisions Despite Imperfect Predictions (2019, result): “We also build on previous work on counterfactual inference and policy learning (Athey & Wager, 2017; Ensign et al., 2018; Gillen et al., 2018; Heidari & Krause, 2018; Joseph et al., 2016; Jung et al., 2018; Kallus, 2018; Kallus & Zhou, 2018; Lakkaraju & Rudin, 2017).”
Working with Beliefs: AI Transparency in the Enterprise (2018, background): “Explainable AI: The usefulness of explainable models has been demonstrated across various application domains such as recommendation systems [16] and healthcare [17], to just name a couple.”
Decoding the Deep: Exploring Class Hierarchies of Deep Representations Using Multiresolution Matrix Factorization (2017, background): “Note that the problem here is not about constructing learning models that model the semantics, like those using statistical relational learning [25] or rule-based methods [6, 18].”
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, methodology): “Causal explanations can be obtained with methods such as causal modeling or approximate decision trees [37, 38, 39, 40].”
Precision Medicine. (2019, background): “…(Linn et al 2017; Wang et al In press); maximizing efficacy subject to constraints on risk/harm (Linn et al 2015; Laber et al 2018; Wang et al In press); and maximizing the mean outcome subject to cost or logistical constraints (Luedtke and van der Laan 2016a; Lakkaraju and Rudin 2017).”