scholarly journals Causal inference for quantile treatment effects

2021 ◽  
Author(s):  
Shuo Sun ◽  
Erica E. M. Moodie ◽  
Johanna G. Nešlehová
Keyword(s):  
Causal Inference ◽  
Treatment Effects ◽  
Quantile Treatment Effects

scholarly journals Limitations of subgroup analysis of underpowered clinical trials for making causal inference about treatment effects

2020 ◽  
Vol 41 (20) ◽  
pp. 1942-1942
Author(s):  
Milad Nazarzadeh ◽  
Kazem Rahimi
Keyword(s):  
Clinical Trials ◽  
Causal Inference ◽  
Subgroup Analysis ◽  
Treatment Effects

scholarly journals Quantile Treatment Effects of College Quality on Earnings: Evidence from Administrative Data in Texas

2012 ◽  
Author(s):  
Rodney Andrews ◽  
Jing Li ◽  
Michael Lovenheim
Keyword(s):  
Administrative Data ◽  
Treatment Effects ◽  
College Quality ◽  
Quantile Treatment Effects

scholarly journals Causal inference and adjustment for reference-arm risk in indirect treatment comparison meta-analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 737-750
Author(s):  
Elyse Swallow ◽  
Oscar Patterson-Lomba ◽  
Rajeev Ayyagari ◽  
Corey Pelletier ◽  
Rina Mehta ◽  
...  
Keyword(s):  
Causal Inference ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Real Data ◽  
Indirect Treatment Comparison ◽  
Common Reference ◽  
Treatment Comparison ◽  
Improve Model ◽  
Indirect Treatment ◽  
Meta Analyses

Aim: To illustrate that bias associated with indirect treatment comparison and network meta-analyses can be reduced by adjusting for outcomes on common reference arms. Materials & methods: Approaches to adjusting for reference-arm effects are presented within a causal inference framework. Bayesian and Frequentist approaches are applied to three real data examples. Results: Reference-arm adjustment can significantly impact estimated treatment differences, improve model fit and align indirectly estimated treatment effects with those observed in randomized trials. Reference-arm adjustment can possibly reverse the direction of estimated treatment effects. Conclusion: Accumulating theoretical and empirical evidence underscores the importance of adjusting for reference-arm outcomes in indirect treatment comparison and network meta-analyses to make full use of data and reduce the risk of bias in estimated treatments effects.

scholarly journals Unconditional Quantile Treatment Effects Under Endogeneity

2013 ◽  
Vol 31 (3) ◽  
pp. 346-357 ◽  
Author(s):  
Markus Frölich ◽  
Blaise Melly
Keyword(s):  
Treatment Effects ◽  
Quantile Treatment Effects

scholarly journals An IV Model of Quantile Treatment Effects

Econometrica ◽  
2005 ◽  
Vol 73 (1) ◽  
pp. 245-261 ◽  
Author(s):  
Victor Chernozhukov ◽  
Christian Hansen
Keyword(s):  
Treatment Effects ◽  
Quantile Treatment Effects ◽  
Iv Model

scholarly journals Statistical methodologies for drawing causal inference from an unreplicated farmlet experiment conducted by the Cicerone Project

2013 ◽  
Vol 53 (8) ◽  
pp. 643 ◽  
Author(s):  
R. Murison ◽  
J. M. Scott
Keyword(s):  
Longitudinal Data ◽  
Statistical Model ◽  
Field Experiment ◽  
Causal Inference ◽  
Statistical Inference ◽  
Treatment Effects ◽  
Management Strategies ◽  
Grazing Management ◽  
Botanical Composition ◽  
Longitudinal Profiles

The present paper explains the statistical inference that can be drawn from an unreplicated field experiment that investigated three different pasture and grazing management strategies. The experiment was intended to assess these three strategies as whole farmlet systems where scale of the experiment precluded replication. The experiment was planned so that farmlets were allocated to matched paddocks on the basis of background variables that were measured across each paddock before the start of the experiment. These conditioning variables were used in the statistical model so that farmlet effects could be discerned from the longitudinal profiles of the responses. The purpose is to explain the principles by which longitudinal data collected from the experiment were interpreted. Two datasets, including (1) botanical composition and (2) hogget liveweights, are used in the present paper as examples. Inferences from the experiment are guarded because we acknowledge that the use of conditioning variables and matched paddocks does not provide the same power as replication. We, nevertheless, conclude that the differences observed are more likely to have been due to treatment effects than to random variation or bias.

scholarly journals An IV Model of Quantile Treatment Effects

2002 ◽  
Author(s):  
Victor Chernozhukov ◽  
Christian Hansen
Keyword(s):  
Treatment Effects ◽  
Quantile Treatment Effects ◽  
Iv Model

scholarly journals Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages

2011 ◽  
Author(s):  
German Blanco ◽  
Carlos A. Flores ◽  
Alfonso Flores-Lagunes
Keyword(s):  
Treatment Effects ◽  
Job Corps ◽  
Quantile Treatment Effects
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