6. A Diagnostic Routine for the Detection of Consequential Heterogeneity of Causal Effects

By S. Morgan, Jennifer Todd. 2008

198 citations recorded by the source corpus.

Subfields: Education, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fe246c69176cd8de95109796e35a742ddf2b5f53

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

1. S. Heil
2. Liza Reisel
3. Lu Cheng
4. P. Richard Hahn
5. Elizabeth A. Stuart
6. Yu Xie
7. Jundong Li
8. Paul A. Attewell

In Statistics and Probability

1. Peter C. Austin
2. Elizabeth A. Stuart
3. Faisal Kamiran
4. Huan Liu
5. P. Richard Hahn
6. Jundong Li
7. Ruocheng Guo
8. Lu Cheng

Citations in the corpus

• Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education (2010, background): “With appropriate weights (Morgan and Todd 2008), we can obtain average treatment effects for specific populations, such as the average treatment effect, the treatment effect on the treated, and the treatment effect on the untreated (Brand and Halaby 2006).”
• What Is Academic Momentum? And Does It Matter? (2012, background): “The first involves weighting on the predicted odds of What Is Academic Momentum? treatment (Morgan & Todd, 2008).”
• The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments (2013, unknown): “1 Zi / 1 ei [45].”
• Age Trends in Daily Social Contact Patterns (2011, methodology): “Fortunately, because ATUS samples are drawn from the Current Population Survey (CPS), there is detailed information about ATUS nonresponders, which makes it possible to employ a complete-case weighting form of missing data adjustment (S. L. Morgan and Todd 2008).”
• Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies (2015, background): “Morgan and Todd suggest that a comparison of characteristics between treated and control subjects should be a step in an analysis of causal treatment effects using IPTW [18].”
• A Survey of Learning Causality with Data: Problems and Methods (2018, methodology): “In addition to estimating ATE, Morgan [93] showed that IPTW naturally lead to unbiased and consistent estimators for the average treatment effect of the treated instances (ATT) and that of the controlled instances (ATC).”
• Causal Discrimination Discovery Through Propensity Score Analysis (2016, background): “oks for tuples of any group. Second, regarding weighting, our key definition of causal risk difference (Definition 2), turns out to be an estimator of the average treatment effect for the treated (ATT) [27]. In our context, the ATT is the expected difference in negative decision (also known as potenti”
• The performance of different propensity score methods for estimating marginal hazard ratios (2007, background): “1 e/ allows one to estimate the average treatment effect in the treated (ATT) [20].”
• An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “The use of IPTW is similar to the use of survey sampling weights that are used to weight survey samples so that they are representative of specific populations (Morgan & Todd, 2008).”