Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

By Yichi Zhang, Eric B. Laber, Anastasios A. Tsiatis, Marie Davidian. 2015

108 citations recorded by the source corpus.

A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high‐quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long‐term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject‐matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if–then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.

Subfields: Statistics and Probability, Causal Inference & Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

In Causal Inference & Discovery

Citations in the corpus

Learning Certifiably Optimal Rule Lists (2017, background): “For example, Falling Rule Lists (Wang and Rudin, 2015) are constrained to have decreasing probabilities down the list as are rule lists for dynamic treat-ment regimes (Zhang et al., 2015) and cost-sensitive dynamic treatment regimes (Lakkaraju and Rudin, 2017).”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “[76], Lakkaraju and Rudin [35] has used the form of rule lists to create optimal treatment regimes, which the MIP framework provided here could be adapted to handle.”
Learning When-to-Treat Policies (2019, methodology): “Throughout this article, we will find that this universality property enables us to both effectively optimize our value 5See Zhang et al. (2015) for an example of using decision lists for policy optimization with a doubly robust estimator.”
Interpretable Dynamic Treatment Regimes (2016, methodology): “Decision rules of this form are a special case of tree-based rules, known as decision lists (Rivest, 1987; Marchand and Sokolova, 2005; Letham et al., 2012; Wang and Rudin, 2015; Zhang et al., 2015), that are immediately interpretable in a domain context as they can be expressed in either flow-chart”
Q-Learning: Theory and Applications (2020, methodology): “In addition to problems of statistical inference, developments for finite Q-learning in statistics have included extensions to nonlinear model classes (Zhao et al. 2009, Laber et al. 2014a, Moodie et al. 2014, Zhang et al. 2015, Xu et al. methods (Zhang et al. 2012, Zhang et al. 2015).”
Learning Cost-Effective and Interpretable Treatment Regimes (2017, unknown): “IPTL turns out to be the best performing baseline in terms of the average outcome, average assessment costs, and average number of characteristics.”