In: Journal of Educational and Behavioral Statistics, 2012, vol. 37, no. 3, p. 443-474
As any empirical method used for causal analysis, social experiments are prone to attrition which may flaw the validity of the results. This paper considers the problem of partially missing outcomes in experiments. Firstly, it systematically reveals under which forms of attrition - in terms of its relation to observable and/or unobservable factors - experiments do (not) yield causal...
|
In: Journal of Econometric Methods, 2019, vol. 8, no. 1, p. 1-20
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the...
|
(Working Papers SES ; 521)
Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-indifferences estimation using a novel dataset of weekly...
|
In: Journal of Applied Econometrics, 2014, vol. 29, no. 6, p. 920-943
This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the...
|
(Working Papers SES ; 496)
This paper considers the evaluation of direct and indirect treatment effects, also known as mediation analysis, when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine sequential conditional independence assumptions on the assignment of the treatment and the mediator, i.e. the variable through which the indirect effect...
|
(Working Papers SES ; 482)
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the...
|
(Working Papers SES ; 466)
This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor...
|