| 1. |
- de Luna, Xavier, 1968-, et al.
(författare)
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Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
- 2014
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Ingår i: Journal of Causal Inference. - : Walter de Gruyter GmbH. - 2193-3677 .- 2193-3685. ; 2:2, s. 187-199
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Tidskriftsartikel (refereegranskat)abstract
- The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.
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| 2. |
- Häggström, Jenny, et al.
(författare)
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Targeted smoothing parameter selection for estimating average causal effects
- 2014
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Ingår i: Computational statistics (Zeitschrift). - : Springer. - 0943-4062 .- 1613-9658. ; 29:6, s. 1727-1748
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Tidskriftsartikel (refereegranskat)abstract
- The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.
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| 3. |
- Persson, Emma, 1981-
(författare)
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Causal inference and case-control studies with applications related to childhood diabetes
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored.An important part of estimating causal effects in an observational study is to control for covariates. The first paper of this thesis concerns the selection of minimal covariate sets sufficient for unconfoundedness of the treatment assignment. A data-driven implementation of two covariate selection algorithms is proposed and evaluated.A common sampling scheme in epidemiology, and when investigating rare events, is the case-control design. In the second paper we study estimators of the marginal causal odds ratio in matched and independent case-control designs. Estimators that, under a logistic regression model, utilize information about the known prevalence of being a case is examined and compared through simulations.The third paper investigates the particular situation where case-control sampled data is reused to estimate the effect of the case-defining event on an outcome of interest. The consequence of ignoring the design when estimating the average causal effect is discussed and a design-weighted matching estimator is proposed. The performance of the estimator is evaluated with simulation experiments, when matching on the covariates directly and when matching on the propensity score.The last paper studies the effect of type 1 diabetes mellitus (T1DM) on school achievements using data from the Swedish Childhood Diabetes Register, a population-based incidence register. We apply theoretical results from the second and third papers in the estimation of the average causal effect within the T1DM population. A matching estimator that accounts for the matched case-control design is used.
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| 4. |
- Stenberg, Anders, 1966-, et al.
(författare)
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Does formal education for older workers increase earnings? : evidence based on rich data and long-term follow up
- 2014
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Ingår i: Labour. - Oxford : John Wiley & Sons. - 1121-7081 .- 1467-9914. ; 28:2, s. 163-189
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Tidskriftsartikel (refereegranskat)abstract
- Governments in Europe, Canada and the US have expressed an ambition to stimulate education of older. In this paper, we analyze if there are effects on annual earnings of formal education for participants aged 42-55 at the time of enrolment in 1994-1995. The analysis explores longitudinal population register data stretching from 1982 to 2007. The method used is difference-in-differences propensity score matching based on a rich set of covariates, including indicators of health and labor market marginalization. Our findings underline the importance of long follow up periods and imply positive effects for females, especially so for women with children, and no significant average earnings effects for males. These results differ from earlier studies but are stable to several alternative assumptions regarding unobservable characteristics. Data further indicate that the gender gap in our estimates may stem from differences in underlying reasons for enrolment.
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| 5. |
- Xavier, de Luna, et al.
(författare)
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Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings
- 2014
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Ingår i: Journal of Applied Statistics. - : Taylor & Francis. - 0266-4763 .- 1360-0532. ; 41:8, s. 1767-1784
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Tidskriftsartikel (refereegranskat)abstract
- We evaluate the effects of college choice on earnings using Swedish register databases. This case study is used to motivate the introduction of a novel procedure to analyse the sensitivity of such an observational study to the assumption made that there are no unobserved confounders – variables affecting both college choice and earnings. This assumption is not testable without further information, and should be considered an approximation of reality. To perform a sensitivity analysis, we measure the departure from the unconfoundedness assumption with the correlation between college choice and earnings when conditioning on observed covariates. The use of a correlation as a measure of dependence allows us to propose a standardised procedure by advocating the use of a fixed value for the correlation, typically 1% or 5%, when checking the sensitivity of an evaluation study. A correlation coefficient is, moreover, intuitive to most empirical scientists, which makes the results of our sensitivity analysis easier to communicate than those of previously proposed methods. In our evaluation of the effects of college choice on earnings, the significantly positive effect obtained could not be questioned by a sensitivity analysis allowing for unobserved confounders inducing at most 5% correlation between college choice and earnings.
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