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 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 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 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 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.

Sharp bounds on causal effects in case-control and cohort studies

Biometrika ◽  
2010 ◽  
Vol 97 (1) ◽  
pp. 123-132 ◽  
Author(s):  
M. Kuroki ◽  
Z. Cai ◽  
Z. Geng
Keyword(s):  
Cohort Studies ◽  
Case Control ◽  
Causal Effects ◽  
Sharp Bounds
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