Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
نویسندگان
چکیده
Alexander G. Nikolaev Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, NY 14260 [email protected] Sheldon H. Jacobson Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801 [email protected] Wendy K. Tam Cho Departments of Political Science and Statistics and the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801 [email protected] Jason J. Sauppe Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801 [email protected] Edward C. Sewell Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, IL 62026 [email protected]
منابع مشابه
An optimization approach for making causal inferences
To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, ...
متن کاملCausal Inference and the Heckman Model
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the purpose of estimating an unbiased causal effect...
متن کاملRobust Nonparametric Testing for Causal Inference in 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 to the standard non...
متن کاملFeature Selection in Structural Health Monitoring Big Data Using a Meta-Heuristic Optimization Algorithm
This paper focuses on the processing of structural health monitoring (SHM) big data. Extracted features of a structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. The PSO-Harmony algorithm is introduced for feature selection to enhance the capability of the proposed method for processing the measure...
متن کاملMatching and Propensity Scores
The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate match...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Operations Research
دوره 61 شماره
صفحات -
تاریخ انتشار 2013