The causal effect of income on health: Evidence from German reunification

We investigate whether therewas a causal effect of income changes on the health satisfaction of East and West Germans in the years following reunification. Our data source is the German Socio-Economic Panel (GSOEP) between 1984 and 2002, and we fit a recently proposed fixed-effects ordinal estimator to our health measures and use a causal decomposition technique to account for panel attrition.We find evidence of a significant positive effect of income changes on health satisfaction, but the quantitative size of this effect is small. This is the case with respect to current income and a measure of ‘permanent’ income.

Dealing with heterogeneity problems and causal effect estimation in entrepreneurship research

This paper deals with causal effect estimation strategies in highly heterogeneous empirical settings such as entrepreneurship. We argue that the clearer used of modern tools developed to deal with the estimation of causal effects in combination with our analysis of different sources of heterogeneity in entrepreneurship can lead to entrepreneurship with higher internal validity. We specifically lend support from the counterfactual logic and modern research of estimation strategies for causal effect estimation.

The causal effect of family size on child labor and education

This paper investigates the causal relationship between family size and child labor and education among brazilian children. More especifically, it analyzes the impact of family size on child labor, school attendance, literacy and school progression. It explores the exogenous variation in family size driven by the presence of twins in the family. The results are consistent under the reasonable assumption that the instrument is a random event. Using the nationally representative brazilian household survey (Pnad), detrimental effects are found on child labor for boys. Moreover, significant effects are obtained for school progression for girls caused by the exogenous presence of the young siblings in the household.

New evidence of the causal effect of family size on child quality in a developing country

This paper presents new evidence of the causal effect of family size on child quality in a developing-country context. We estimate the impact of family size on child labor and educational outcomes among Brazilian children and young adults by exploring the exogenous variation of family size driven by the presence of twins in the family. Using the Brazilian Census data for 1991, we nd that the exogenous increase in family size is positively related to labor force participation for boys and girls and to household chores for young women. We also and negative e ects on educational outcomes for boys and girls and negative impacts on human capital formation for young female adults. Moreover, we obtain suggestive evidence that credit and time constraints faced by poor families may explain the findings.

A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV Cohort Study

When estimating the effect of treatment on HIV using data from observational studies, standard methods may produce biased estimates due to the presence of time-dependent confounders. Such confounding can be present when a covariate, affected by past exposure, is both a predictor of the future exposure and the outcome. One example is the CD4 cell count, being a marker for disease progression for HIV patients, but also a marker for treatment initiation and influenced by treatment. Fitting a marginal structural model (MSM) using inverse probability weights is one way to give appropriate adjustment for this type of confounding. In this paper we study a simple and intuitive approach to estimate similar treatment effects, using observational data to mimic several randomized controlled trials. Each 'trial' is constructed based on individuals starting treatment in a certain time interval. An overall effect estimate for all such trials is found using composite likelihood inference. The method offers an alternative to the use of inverse probability of treatment weights, which is unstable in certain situations. The estimated parameter is not identical to the one of an MSM, it is conditioned on covariate values at the start of each mimicked trial. This allows the study of questions that are not that easily addressed fitting an MSM. The analysis can be performed as a stratified weighted Cox analysis on the joint data set of all the constructed trials, where each trial is one stratum. The model is applied to data from the Swiss HIV cohort study.

The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring

We examined the effect of switching to second-line antiretroviral therapy (ART) on mortality in patients who experienced immunological failure in ART programmes without access to routine viral load monitoring in sub-Saharan Africa.

Quantifying the improvement in sepsis diagnosis, documentation, and coding: the marginal causal effect of year of hospitalization on sepsis diagnosis.

PURPOSE To quantify the coinciding improvement in the clinical diagnosis of sepsis, its documentation in the electronic health records, and subsequent medical coding of sepsis for billing purposes in recent years. METHODS We examined 98,267 hospitalizations in 66,208 patients who met systemic inflammatory response syndrome criteria at a tertiary care center from 2008 to 2012. We used g-computation to estimate the causal effect of the year of hospitalization on receiving an International Classification of Diseases, Ninth Revision, Clinical Modification discharge diagnosis code for sepsis by estimating changes in the probability of getting diagnosed and coded for sepsis during the study period. RESULTS When adjusted for demographics, Charlson-Deyo comorbidity index, blood culture frequency per hospitalization, and intensive care unit admission, the causal risk difference for receiving a discharge code for sepsis per 100 hospitalizations with systemic inflammatory response syndrome, had the hospitalization occurred in 2012, was estimated to be 3.9% (95% confidence interval [CI], 3.8%-4.0%), 3.4% (95% CI, 3.3%-3.5%), 2.2% (95% CI, 2.1%-2.3%), and 0.9% (95% CI, 0.8%-1.1%) from 2008 to 2011, respectively. CONCLUSIONS Patients with similar characteristics and risk factors had a higher of probability of getting diagnosed, documented, and coded for sepsis in 2012 than in previous years, which contributed to an apparent increase in sepsis incidence.

The effect of alcohol and drug consumption on academic performance : a treatment effect evaluation

It is often argued that consumption of alcohol, tobacco and drugs is detrimental to the cognitive abilities of teenagers. In order to disentangle a possible causal effect of these substances use from a self-selection bias, we control for pupils previous performance and for their previous rate of progression applying a DiDiD strategy. Using the NELS 1988 panel dataset, we find that the effects of alcohol and tobacco on test scores disappear once the selection bias is controlled for (this does not preclude long term detrimental effects). However, we find reliable evidence that heavy use of drugs (marijuana and cocaine) has direct detrimental effects on educational achievements. Hence, our results may have significant policy implications.

Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey

© Institute of Mathematical Statistics, 2014.Motivated by recent findings in the field of consumer science, this paper evaluates the causal effect of debit cards on household consumption using population-based data from the Italy Survey on Household Income and Wealth (SHIW). Within the Rubin Causal Model, we focus on the estimand of population average treatment effect for the treated (PATT). We consider three existing estimators, based on regression, mixed matching and regression, propensity score weighting, and propose a new doubly-robust estimator. Semiparametric specification based on power series for the potential outcomes and the propensity score is adopted. Cross-validation is used to select the order of the power series. We conduct a simulation study to compare the performance of the estimators. The key assumptions, overlap and unconfoundedness, are systematically assessed and validated in the application. Our empirical results suggest statistically significant positive effects of debit cards on the monthly household spending in Italy.

Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

Counterfactuals and causal inference: Methods and principles for social research

"Most quantitative empirical analyses are motivated by the desire to estimate the causal effect of an independent variable on a dependent variable. Although the randomized experiment is the most powerful design for this task, in most social science research done outside of psychology, experimental designs are infeasible. (Winship & Morgan, 1999, p. 659)." This quote from earlier work by Winship and Morgan, which was instrumental in setting the groundwork for their book, captures the essence of our review of Morgan and Winship's book: It is about causality in nonexperimental settings.

The program and treatment effect of summer jobs on girls’ post-schooling incomes

Public programs (of disputed effect) offering summer jobs or work while in high school to smooth the transition from school to work is commonplace. In this paper, 1447 girls in their first grade of high school between 1997-2003 and randomly allotted summer jobs via a program in Falun (Sweden) are followed 5-12 years after graduation. The program led to a substantially larger accumulation of income while in high school. The causal effect of the high school income on post-schooling incomes was substantial and statistically significant. The implied elasticity of 0.4 is however potentially inflated dueto heterogeneous effects.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.