Sharp bounds on the causal effects in randomized experiments with “truncation-by-death”

2008 ◽  
Vol 78 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Kosuke Imai
Keyword(s):  
Causal Effects ◽  
Randomized Experiments ◽  
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 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.

Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”

2003 ◽  
Vol 28 (4) ◽  
pp. 353-368 ◽  
Author(s):  
Junni L. Zhang ◽  
Donald B. Rubin
Keyword(s):  
Sample Space ◽  
Causal Effects ◽  
Outcome Variable ◽  
Principal Stratification ◽  
Randomized Experiments ◽  
Large Sample ◽  
Traditional Approaches

The topic of “truncation by death” in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither “censored” nor “missing,” but should be treated as being defined on an extended sample space. Using an educational example to illustrate, we will outline here a formulation for tackling this issue, where we call the outcome “truncated by death” because there is no hidden value of the outcome variable masked by the truncating event. We first formulate the principal stratification ( Frangakis & Rubin, 2002 ) approach, and we then derive large sample bounds for causal effects within the principal strata, with or without various identification assumptions. Extensions are then briefly discussed.

scholarly journals Sharp bounds on the variance in randomized experiments

2014 ◽  
Vol 42 (3) ◽  
pp. 850-871 ◽  
Author(s):  
Peter M. Aronow ◽  
Donald P. Green ◽  
Donald K. K. Lee
Keyword(s):  
Randomized Experiments ◽  
Sharp Bounds

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 Estimating slim-majority effects in US state legislatures with a regression discontinuity design under local randomization assumptions

2020 ◽  
pp. 1-10
Author(s):  
Leandro De Magalhães
Keyword(s):  
Finite Number ◽  
Regression Discontinuity ◽  
State Legislatures ◽  
State Level ◽  
Variable Number ◽  
Causal Effects ◽  
Regression Discontinuity Design ◽  
Randomized Experiments ◽  
Majority Party ◽  
Empirical Tests

Abstract Regression discontinuity design could be a valuable tool for identifying causal effects of a given party holding a legislative majority. However, the variable “number of seats” takes a finite number of values rather than a continuum and, hence, it is not suited as a running variable. Recent econometric advances suggest the necessary assumptions and empirical tests that allow us to interpret small intervals around the cut-off as local randomized experiments. These permit us to bypass the assumption that the running variable must be continuous. Herein, we implement these tests for US state legislatures and propose another: whether a slim-majority of one seat had at least one state-level district result that was itself a close race won by the majority party.

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
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