scholarly journals Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification

2016 ◽  
Vol 66 (4) ◽  
pp. 759-776
Author(s):  
Andrea Mercatanti ◽  
Fan Li
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Principal Stratification ◽  
Cash Demand ◽  
Debit Cards

scholarly journals A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies

2010 ◽  
Vol 7 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Brian L Egleston ◽  
Karen L Cropsey ◽  
Amy B Lazev ◽  
Carolyn J Heckman
Keyword(s):  
Sensitivity Analysis ◽  
Smoking Cessation ◽  
Principal Stratification ◽  
Analysis Application

scholarly journals Principal Stratification in Causal Inference

Biometrics ◽  
2002 ◽  
Vol 58 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Constantine E. Frangakis ◽  
Donald B. Rubin
Keyword(s):  
Causal Inference ◽  
Principal Stratification

Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data

2013 ◽  
Vol 21 (2) ◽  
pp. 233-251 ◽  
Author(s):  
Walter R. Mebane ◽  
Paul Poast
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Treatment Effect ◽  
Bayesian Method ◽  
Treatment Effects ◽  
Applied Research ◽  
Average Treatment Effect ◽  
Potential Importance ◽  
Treatment Data

How a treatment causes a particular outcome is a focus of inquiry in political science. When treatment data are either nonrandomly assigned or missing, the analyst will often invoke ignorability assumptions: that is, both the treatment and missingness are assumed to be as if randomly assigned, perhaps conditional on a set of observed covariates. But what if these assumptions are wrong? What if the analyst does not know why—or even if—a particular subject received a treatment? Building on Manski, Molinari offers an approach for calculating nonparametric identification bounds for the average treatment effect of a binary treatment under general missingness or nonrandom assignment. To make these bounds substantively more informative, Molinari's technique permits adding monotonicity assumptions (e.g., assuming that treatment effects are weakly positive). Given the potential importance of these assumptions, we develop a new Bayesian method for performing sensitivity analysis regarding them. This sensitivity analysis allows analysts to interpret the assumptions' consequences quantitatively and visually. We apply this method to two problems in political science, highlighting the method's utility for applied research.

scholarly journals A Selection Bias Approach to Sensitivity Analysis for Causal Effects

2014 ◽  
Vol 22 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Matthew Blackwell
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Selection Bias ◽  
Large Volume ◽  
Quantitative Approach ◽  
Causal Effects ◽  
Omitted Variables ◽  
Fundamental Fact ◽  
Unmeasured Confounders

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

scholarly journals Sensitivity analysis for causal inference using inverse probability weighting

2011 ◽  
Vol 53 (5) ◽  
pp. 822-837 ◽  
Author(s):  
Changyu Shen ◽  
Xiaochun Li ◽  
Lingling Li ◽  
Martin C. Were
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability
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