'Average treatment effect' presentation slideshows

Session 1

Methodology Research Group. Methods of explanatory analysis for psychological treatment trials workshop. Session 1 Introduction to causal inference and the analysis of treatment effects in the presence of departures from random allocation Ian White. Funded by: MRC Methodology Grant G0600555

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Presentation Outline

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An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA

Hielke Buddelmeyer Melbourne Institute of Applied Economic and Social Research Hielkeb@unimelb.edu.au. Emmanuel Skoufias The World Bank eskoufias@worldbank.org. An Evaluation of the Performance of Regression Discontinuity Design on PROGRESA. Introduction.

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Sebastian Galiani November 2006

Causal Inference. Sebastian Galiani November 2006. Motivation. The research questions that motivate most studies in the health sciences are causal in nature. For example:

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Confounding adjustment: Ideas in Action -a case study

Confounding adjustment: Ideas in Action -a case study. Xiaochun Li, Ph.D. Associate Professor Division of Biostatistics Indiana University School of Medicine . Outline. Description of the data set Quantity to be estimated Summary of baseline characteristics Approaches to data analyses

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Confounding adjustment: Ideas in Action -a case study

Confounding adjustment: Ideas in Action -a case study. Xiaochun Li, Ph.D. Associate Professor Division of Biostatistics Indiana University School of Medicine. Outline. Description of the data set Quantity to be estimated Summary of baseline characteristics Approaches to data analyses

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The Fundamental Problem of Causal Inference

The Fundamental Problem of Causal Inference. Alexander Tabarrok January 2007. The Gold Standard. The gold standard is randomization. If units are randomly assigned to treatment then the selection effect disappears.

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Cross-national comparative research with longitudinal data: Understanding youth poverty

Cross-national comparative research with longitudinal data: Understanding youth poverty . Maria Iacovou (ISER) with Arnstein Aassve, Maria Davia, Letizia Mencarini, Stefano Mazzucco

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INSTRUMENTAL VARIABLES

INSTRUMENTAL VARIABLES. Technical Track Session IV.

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How labor markets and welfare policies shape health across the life course:

The potentials and limitations of natural policy experiments. Kate Strully University at Albany, SUNY kstrully@albany.edu. How labor markets and welfare policies shape health across the life course: .

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Review of Identifying Causal Effects

Review of Identifying Causal Effects. Methods of Economic Investigation Lecture 13. Last Term. Classical OLS has 5 main assumptions A1. Full Rank: X is a T x k matrix with rank p(X) = k?n A2. Linearity: y = X ? + ? where E( ? ) = 0

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Defined Population

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LT6: IV2

LT6: IV2. Sam Marden s.h.marden@lse.ac.uk. Question 1 & 2. We estimate the following demand equation ln ( packpc ) = b 0 + b 1 ln( avgprs ) +u What do we require for a consistent estimate of b 1 ? What is the biggest problem we are likely to face ? What is the likely direction of bias?

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Prepared for: Agency for Healthcare Research and Quality (AHRQ) www.ahrq.gov

Estimation and Reporting of Heterogeneity of Treatment Effects in Observational Comparative Effectiveness Research. Prepared for: Agency for Healthcare Research and Quality (AHRQ) www.ahrq.gov. Outline of Material. This presentation will:

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Propensity Score

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