scholarly journals Sharp Bounds on Causal Effects under Sample Selection

2013 ◽  
Vol 77 (1) ◽  
pp. 129-151 ◽  
Author(s):  
Martin Huber ◽  
Giovanni Mellace
Keyword(s):  
Sample Selection ◽  
Causal Effects ◽  
Sharp Bounds

scholarly journals Heterogeneous Causal Effects and Sample Selection Bias

2015 ◽  
Vol 2 ◽  
pp. 351-369 ◽  
Author(s):  
Richard Breen ◽  
Seungsoo Choi ◽  
Anders Holm
Keyword(s):  
Selection Bias ◽  
Sample Selection ◽  
Causal Effects ◽  
Sample Selection Bias

scholarly journals Bounds on treatment effects in regression discontinuity designs with a manipulated running variable

2020 ◽  
Vol 11 (3) ◽  
pp. 839-870 ◽  
Author(s):  
François Gerard ◽  
Miikka Rokkanen ◽  
Christoph Rothe
Keyword(s):  
Unemployment Insurance ◽  
General Model ◽  
Regression Discontinuity ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Binary Decision ◽  
Sharp Bounds ◽  
Wide Range ◽  
Regression Discontinuity Designs ◽  
Disincentive Effect

The key assumption in regression discontinuity analysis is that the distribution of potential outcomes varies smoothly with the running variable around the cutoff. In many empirical contexts, however, this assumption is not credible; and the running variable is said to be manipulated in this case. In this paper, we show that while causal effects are not point identified under manipulation, one can derive sharp bounds under a general model that covers a wide range of empirical patterns. The extent of manipulation, which determines the width of the bounds, is inferred from the data in our setup. Our approach therefore does not require making a binary decision regarding whether manipulation occurs or not, and can be used to deliver manipulation‐robust inference in settings where manipulation is conceivable, but not obvious from the data. We use our methods to study the disincentive effect of unemployment insurance on (formal) reemployment in Brazil, and show that our bounds remain informative, despite the fact that manipulation has a sizable effect on our estimates of causal parameters.

scholarly journals Selection Without Exclusion

Econometrica ◽  
2020 ◽  
Vol 88 (3) ◽  
pp. 1007-1029
Author(s):  
Bo E. Honoré ◽  
Luojia Hu
Keyword(s):  
Line Segment ◽  
Sample Selection ◽  
Selection Model ◽  
Sharp Bounds ◽  
One Dimensional ◽  
Explanatory Variables ◽  
Sample Selection Model ◽  
Full Structure ◽  
Exclusion Restrictions ◽  
Parameter Values

It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. The additional structure can significantly reduce the identified set for the parameters of interest. Specifically, we construct the identified set for the parameter vector of interest. It is a one‐dimensional line segment in the parameter space, and we demonstrate that this line segment can be short in practice. We show that the identified set is sharp when the model is correct and empty when there exist no parameter values that make the sample selection model consistent with the data. We also provide non‐sharp bounds under the assumption that the model is correct. These are easier to compute and associated with lower statistical uncertainty than the sharp bounds. Throughout the paper, we illustrate our approach by estimating a standard sample selection model for wages.

Sharp Bounds on Survivor Average Causal Effects When the Outcome Is Binary and Truncated by Death

2016 ◽  
Vol 7 (2) ◽  
pp. 1-11
Author(s):  
Na Shan ◽  
Xiaogang Dong ◽  
Pingfeng Xu ◽  
Jianhua Guo
Keyword(s):  
Causal Effects ◽  
Sharp Bounds

scholarly journals Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds

2018 ◽  
Vol 43 (5) ◽  
pp. 540-567 ◽  
Author(s):  
Jiannan Lu ◽  
Peng Ding ◽  
Tirthankar Dasgupta
Keyword(s):  
Causal Effect ◽  
Real Life ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Randomized Experiments ◽  
Sharp Bounds ◽  
Average Causal Effect ◽  
Behavioral Studies ◽  
And Control ◽  
Potential Outcomes Framework

Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the parameter of interest, is difficult to interpret for ordinal outcomes. To address this challenge, we propose to use two causal parameters, which are defined as the probabilities that the treatment is beneficial and strictly beneficial for the experimental units. However, although well-defined for any outcomes and of particular interest for ordinal outcomes, the two aforementioned parameters depend on the association between the potential outcomes and are therefore not identifiable from the observed data without additional assumptions. Echoing recent advances in the econometrics and biostatistics literature, we present the sharp bounds of the aforementioned causal parameters for ordinal outcomes, under fixed marginal distributions of the potential outcomes. Because the causal estimands and their corresponding sharp bounds are based on the potential outcomes themselves, the proposed framework can be flexibly incorporated into any chosen models of the potential outcomes and is directly applicable to randomized experiments, unconfounded observational studies, and randomized experiments with noncompliance. We illustrate our methodology via numerical examples and three real-life applications related to educational and behavioral research.

scholarly journals Who’s in and Who’s Out? Selection Bias in Aging Research

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 822-822
Author(s):  
Elizabeth Rose Mayeda ◽  
Eleanor Hayes-Larson ◽  
Hailey Banack
Keyword(s):  
Selection Bias ◽  
External Validity ◽  
Sample Selection ◽  
Internal Validity ◽  
Causal Effects ◽  
Aging Research ◽  
Selection Processes ◽  
The People ◽  
Biased Estimates ◽  
Internal And External Validity

Abstract Selection bias presents a major threat to both internal and external validity in aging research. “Selection bias” refers to sample selection processes that lead to statistical associations in the study sample that are biased estimates of causal effects in the population of interest. These processes can lead to: (1) results that do not generalize to the population of interest (threat to external validity) or (2) biased effect estimates (associations that do not represent causal effects for any population, including the people in the sample; a threat to internal validity). In this presentation, we give an overview of selection bias in aging research. We will describe processes that can give rise to selection bias, highlight why they are particularly pervasive in this field, and present several examples of selection bias in aging research. We end with a brief summary of strategies to prevent and correct for selection bias in aging research.

scholarly journals Monotone Confounding, Monotone Treatment Selection and Monotone Treatment Response

2014 ◽  
Vol 2 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zhichao Jiang ◽  
Yasutaka Chiba ◽  
Tyler J. VanderWeele
Keyword(s):  
Treatment Response ◽  
Instrumental Variables ◽  
Treatment Selection ◽  
Causal Effects ◽  
Treatment Variable ◽  
Unmeasured Confounding ◽  
Returns To Schooling ◽  
Sharp Bounds

AbstractManski (Monotone treatment response. Econometrica 1997;65:1311–34) and Manski and Pepper (Monotone instrumental variables: with an application to the returns to schooling. Econometrica 2000;68:997–1010) gave sharp bounds on causal effects under the assumptions of monotone treatment response (MTR) and monotone treatment selection (MTS). VanderWeele (The sign of the bias of unmeasured confounding. Biometrics 2008;64:702–6) provided bounds for binary treatment under an assumption of monotone confounding (MC). We discuss the relation between MC and MTS and provide bounds under various combinations of these assumptions. We show that MC and MTS coincide for a binary treatment, but MC does not imply MTS for a treatment variable with more than two levels.

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