causal inference – Optimization Online

Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment

Published: 2023/11/24

Applications - OR and Management Sciences causal inference, data-driven optimization, efficiency, fairness, homelessness, scarce resource allocation

We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online … Read more

Learning Optimal Prescriptive Trees from Observational Data

Published: 2021/08/26, Updated: 2023/09/22

(Mixed) Integer Linear Programming, Applications - OR and Management Sciences causal inference, interpretability, mixed-integer programming, observational data, prescriptive trees

We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data. This problem arises in numerous socially important domains such as public health and personalized medicine, where interpretable and data-driven interventions are sought based on data gathered … Read more

Robust Nonparametric Testing for Causal Inference in Observational Studies

Published: 2015/12/08

Md. Noor-E-Alam

Cynthia Rudin

(Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Data-Mining causal inference, discrete optimization, integer programming, matched pairs design, nonparametric hypothesis test, observational studies

We consider the decision problem of making causal conclusions from observational data. Typically, using standard matched pairs techniques, there is a source of uncertainty that is not usually quantified, namely the uncertainty due to the choice of the experimenter: two different reasonable experimenters can easily have opposite results. In this work we present an alternative … Read more

Robust Testing for Causal Inference in Observational Studies

Published: 2015/03/14, Updated: 2015/12/08

Md. Noor-E-Alam

Cynthia Rudin

(Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Statistics causal inference, discrete optimization, hypothesis test, integer programming, matched pairs design, observational studies

A vast number of causal inference studies use matching techniques, where treatment cases are matched with similar control cases. For observational data in particular, we claim there is a major source of uncertainty that is essentially ignored in these tests, which is the way the assignments of matched pairs are constructed. It is entirely possible, … Read more