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Average Treatment Effects (average + treatment_effects)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting40%

Selected Abstracts

The relationship between baseline value and its change: problems in categorization and the proposal of a new method

EUROPEAN JOURNAL OF ORAL SCIENCES, Issue 4 2005
Yu-Kang Tu
Oral health researchers have shown great interest in the relationship between the initial status of diseases and subsequent changes following treatment. Two main approaches have been adopted to provide evidence of a positive association between baseline values and their changes following treatment. One approach is to use correlation or regression to test the relationship between baseline measurements and subsequent change (correlation/regression approach). The second approach is to categorize the lesions into subgroups, according to threshold values, and subsequently compare the treatment effects across the two (or more) subgroups (categorization approach). However, the correlation/regression approach suffers a methodological weakness known as mathematical coupling. Consequently, the statistical procedure of testing the null hypothesis becomes inappropriate. Categorization seems to avoid the problem of mathematical coupling, although it still suffers regression to the mean. We show, first, how the appropriate null hypothesis may be established to analyze the relationship between baseline values and change in the correlation approach and, second, we use computer simulations to investigate the impact of regression to the mean on the significance testing of the differences in the average treatment effects (or average baseline values) in the categorization approach. Data available from previous literature are reanalyzed by testing the appropriate null hypotheses and the results are compared to those from testing the usual (incorrect) null hypothesis. The results indicate that both the correlation and categorization approaches can give rise to misleading conclusions and that more appropriate methods, such as Oldham's method and our new approach of deriving the correct null hypothesis, should be adopted. [source]

Programme Evaluation with Multiple Treatments

JOURNAL OF ECONOMIC SURVEYS, Issue 2 2004
Markus Frölich
Abstract., This paper reviews the main identification and estimation strategies for microeconometric policy evaluation. Particular emphasis is laid on evaluating policies consisting of multiple programmes, which is of high relevance in practice. For example, active labour market policies may consist of different training programmes, employment programmes and wage subsidies. Similarly, sickness rehabilitation policies often offer different vocational as well as non-vocational rehabilitation measures. First, the main identification strategies (control-for-confounding-variables, difference-in-difference, instrumental-variable, and regression-discontinuity identification) are discussed in the multiple-programme setting. Thereafter, the different nonparametric matching and weighting estimators of the average treatment effects and their properties are examined. [source]

Empirical-likelihood-based difference-in-differences estimators

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2008
Jing Qin
Summary., Recently there has been a surge in econometric and epidemiologic works focusing on estimating average treatment effects under various sets of assumptions. Estimation of average treatment effects in observational studies often requires adjustment for differences in pretreatment variables. Rosenbaum and Rubin have proposed the propensity score method for estimating the average treatment effect by adjusting pretreatment variables. In this paper, the empirical likelihood method is used to estimate average treatment effects on the treated under the difference-in-differences framework. The advantage of this approach is that the common marginal covariate information can be incorporated naturally to enhance the estimation of average treatment effects. Compared with other approaches in the literature, the method proposed can provide more efficient estimation. A simulation study and a real economic data analysis are presented. [source]

Dragon Children: Identifying the Causal Effect of the First Child on Female Labour Supply with the Chinese Lunar Calendar,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2008
James P. Vere
Abstract Instrumental variables (IV) estimates of the effect of fertility on female labour supply have only been able to identify the causal effect of second and higher parity children. This study uses exogenous variation in fertility caused by the Chinese lunar calendar to identify the effect of the first child. Additionally, weighting formulas are presented to interpret IV estimates as weighted average treatment effects in the case of multiple endogenous variables, which are useful when children vary in intensity by both number and age. The effect of the first child is found to be much greater than that of other children. [source]