Estimating the effect of smoking on birth outcomes using a matched panel data approach

By Jason Abrevaya. 2006

113 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Gender Studies, General Health Professions

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “We identify multiple large groups with odds ratios that are substantially below 1, which suggests that the available data are not sufficient for granular analysis, or any strong conclusion on NICU admissions, since it is unlikely that there is a protective effect of smoking on NICU admissions (Abrev”
Estimating Conditional Average Treatment Effects (2014, background): “Most notably, Abrevaya and Dahl (2008) estimated the effect of smoking separately for various quantiles of the birth weight distribution.”
Identifying distributional characteristics in random coefficients panel data models (2009, methodology): “…a time-invariant distribution of treatment effects across units.3 For example, in our empirical application, we look at the extent of heterogeneity in the effect of smoking during pregnancy on children outcomes at birth, building on Abrevaya (2006)’s results for mothers with multiple births.”
Within and between Estimates in Random-Effects Models: Advantages and Drawbacks of Correlated Random Effects and Hybrid Models (2013, methodology): “Smoking is likely to be correlated with other mother-specific unobserved variables (Abrevaya 2006) that adversely impact birth weight.”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “In a recent paper, Abrevaya, Hsu, and Lieli (2015) consider the estimation of the CATEF(1); however, there are two main differences of this paper relative to Abrevaya, Hsu, and Lieli (2015). First, we propose the doubly robust procedure to estimate the CATEF. Abrevaya, Hsu, and Lieli (2015) consider”
Nonparametric estimation of causal heterogeneity under high-dimensional confounding (2019, methodology): “Results were obtained following the procedures in Abrevaya et al. (2015) and Lee et al.”
Nonlinear Panel Data Estimation via Quantile Regressions (2015, methodology): “Abrevaya (2006) uses a mother fixed-effects approach to address endogeneity of smoking.”
The Effects of Birth Inputs on Birthweight (2008, methodology): “The issue of feedback effects is discussed at length in Abrevaya (2006) in the context of a conditional-expectation model, where instrumentalvariables methods (using lagged birthweights as instruments) can be utilized.”
Interactions in Fixed Effects Regression Models (2020, unknown): “11 Specifically, we used infant birth weight data provided by Stata Press for training issues (see the attached syntax; see also Abrevaya 2006); data from the Socio-Economic Panel Study (SOEP, see Giesselmann et al. 2019) v.27.2 (doi:10.5684/soep.v27.”