scholarly journals Simultaneous record linkage and causal inference with propensity score subclassification

2018 ◽  
Vol 37 (24) ◽  
pp. 3533-3546
Author(s):  
Joan Heck Wortman ◽  
Jerome P. Reiter
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Record Linkage ◽  
Simultaneous Record

scholarly journals Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

2018 ◽  
Vol 38 (8) ◽  
pp. 1442-1458 ◽  
Author(s):  
Jing Qin ◽  
Tao Yu ◽  
Pengfei Li ◽  
Hao Liu ◽  
Baojiang Chen
Keyword(s):  
Missing Data ◽  
Propensity Score ◽  
Causal Inference ◽  
Single Index ◽  
Index Model ◽  
Single Index Model

scholarly journals On adaptive propensity score truncation in causal inference

2018 ◽  
Vol 28 (6) ◽  
pp. 1741-1760 ◽  
Author(s):  
Cheng Ju ◽  
Joshua Schwab ◽  
Mark J van der Laan
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Extreme Values ◽  
Likelihood Estimation ◽  
Experimental Treatment ◽  
Point Estimation ◽  
Finite Sample ◽  
Novel Approach ◽  
Confidence Interval Coverage ◽  
Interval Coverage

The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.

scholarly journals Why Propensity Scores Should Not Be Used for Matching

2019 ◽  
Vol 27 (4) ◽  
pp. 435-454 ◽  
Author(s):  
Gary King ◽  
Richard Nielsen
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Propensity Score Matching ◽  
Propensity Scores ◽  
Original Data ◽  
The Other ◽  
Randomized Experiment ◽  
Random Matching ◽  
Popular Method ◽  
Matching Methods

We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.

scholarly journals Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference

2016 ◽  
Vol 12 (1) ◽  
pp. 97-115 ◽  
Author(s):  
Mireille E. Schnitzer ◽  
Judith J. Lok ◽  
Susan Gruber
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Causal Inference ◽  
Simulation Study ◽  
Learning Approaches ◽  
Minimum Loss ◽  
Knowledge Based ◽  
Flexible Modeling ◽  
Selection For ◽  
Highly Correlated

Abstract This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

scholarly journals Tuning Random Forests for Causal Inference Under Cluster-Level Unmeasured Confounding

2020 ◽  
Author(s):  
Youmi Suk ◽  
Hyunseung Kang
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Random Forests ◽  
Fixed Effects ◽  
Propensity Scores ◽  
Real Data ◽  
Unmeasured Confounding ◽  
Variable Bias ◽  
Almost All ◽  
Cluster Level

Recently, there has been growing interest in using machine learning (ML) methods for causal inference due to their automatic and flexible abilities to model the propensity score and the outcome model. However, almost all the ML methods for causal inference have been studied under the assumption of no unmeasured confounding and there is little work on handling omitted/unmeasured variable bias. This paper focuses on an ML method based on random forests known as Causal Forests and presents five simple modifications for tuning Causal Forests so that they are robust to cluster-level unmeasured confounding. Our simulation study finds that adjusting the algorithm with the propensity score from fixed effects logistic regression and using demeaned variables make the estimates more robust to cluster-level unmeasured confounding. In particular, using demeaned variables is useful when we are not sure of the functional form of the propensity scores. We conclude by demonstrating our proposals in a real data study concerning the effect of taking an eighth-grade algebra course on math achievement scores from the Early Childhood Longitudinal Study.

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