scholarly journals Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shirley X. Liao ◽  
Lucas Henneman ◽  
Cory Zigler
Keyword(s):  
American Statistical Association ◽  
Treatment Patterns ◽  
Average Treatment Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Medicare Beneficiaries ◽  
Finite Sample ◽  
Treatment Assignment ◽  
Power Plant Emissions ◽  
Plant Emissions

AbstractMarginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the ATE on the overlap population (ATO) which is estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within an MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) (Zigler, C. M., and M. Cefalu. 2017. “Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap.” eprint arXiv:1710.08749.) as well as a weighting analog (Li, F., K. L. Morgan, and A. M. Zaslavsky. 2018. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113: 390–400, https://doi.org/10.1080/01621459.2016.1260466.) to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency, and coverage. Finally, an analysis using these methods is performed on Medicare beneficiaries residing across 18,480 ZIP codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease (IHD) hospitalization, accounting for seasonal patterns that lead to change in treatment over time.

G-estimation of structural nested mean models for competing risks data using pseudo-observations

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 860-875 ◽  
Author(s):  
Shiro Tanaka ◽  
M Alan Brookhart ◽  
Jason P Fine
Keyword(s):  
Type 2 Diabetes ◽  
Competing Risks ◽  
Causal Effects ◽  
Time Varying ◽  
Finite Sample ◽  
Subdistribution Hazard ◽  
Hazard Ratios ◽  
Competing Risks Data ◽  
Risk Ratios

Summary This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented.

scholarly journals Development of an Understanding of Reactive Mercury in Ambient Air: A Review

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Mae Sexauer Gustin ◽  
Sarrah M. Dunham-Cheatham ◽  
Jiaoyan Huang ◽  
Steve Lindberg ◽  
Seth N. Lyman
Keyword(s):  
Ambient Air ◽  
Automated System ◽  
Current Status ◽  
Measurement Systems ◽  
Power Plant Emissions ◽  
History Of ◽  
Plant Emissions ◽  
Gas Measurements ◽  
Future Work ◽  
Stack Gas

This review focuses on providing the history of measurement efforts to quantify and characterize the compounds of reactive mercury (RM), and the current status of measurement methods and knowledge. RM collectively represents gaseous oxidized mercury (GOM) and that bound to particles. The presence of RM was first recognized through measurement of coal-fired power plant emissions. Once discovered, researchers focused on developing methods for measuring RM in ambient air. First, tubular KCl-coated denuders were used for stack gas measurements, followed by mist chambers and annular denuders for ambient air measurements. For ~15 years, thermal desorption of an annular KCl denuder in the Tekran® speciation system was thought to be the gold standard for ambient GOM measurements. Research over the past ~10 years has shown that the KCl denuder does not collect GOM compounds with equal efficiency, and there are interferences with collection. Using a membrane-based system and an automated system—the Detector for Oxidized mercury System (DOHGS)—concentrations measured with the KCl denuder in the Tekran speciation system underestimate GOM concentrations by 1.3 to 13 times. Using nylon membranes it has been demonstrated that GOM/RM chemistry varies across space and time, and that this depends on the oxidant chemistry of the air. Future work should focus on development of better surfaces for collecting GOM/RM compounds, analytical methods to characterize GOM/RM chemistry, and high-resolution, calibrated measurement systems.

scholarly journals Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Weixin Cai ◽  
Mark van der Laan
Keyword(s):  
Confidence Intervals ◽  
Nonparametric Estimation ◽  
Cross Validation ◽  
Data Distribution ◽  
Average Treatment Effect ◽  
Finite Sample ◽  
Nonparametric Bootstrap ◽  
A Value ◽  
Variation Norm ◽  
Selection Operator

AbstractThe Highly-Adaptive least absolute shrinkage and selection operator (LASSO) Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise differentiable parameter in a statistical model that at minimal (and possibly only) assumes that the sectional variation norm of the true nuisance functions (i.e., relevant part of data distribution) are finite. It relies on an initial estimator (HAL-MLE) of the nuisance functions by minimizing the empirical risk over the parameter space under the constraint that the sectional variation norm of the candidate functions are bounded by a constant, where this constant can be selected with cross-validation. In this article we establish that the nonparametric bootstrap for the HAL-TMLE, fixing the value of the sectional variation norm at a value larger or equal than the cross-validation selector, provides a consistent method for estimating the normal limit distribution of the HAL-TMLE. In order to optimize the finite sample coverage of the nonparametric bootstrap confidence intervals, we propose a selection method for this sectional variation norm that is based on running the nonparametric bootstrap for all values of the sectional variation norm larger than the one selected by cross-validation, and subsequently determining a value at which the width of the resulting confidence intervals reaches a plateau. We demonstrate our method for 1) nonparametric estimation of the average treatment effect when observing a covariate vector, binary treatment, and outcome, and for 2) nonparametric estimation of the integral of the square of the multivariate density of the data distribution. In addition, we also present simulation results for these two examples demonstrating the excellent finite sample coverage of bootstrap-based confidence intervals.

The Benefit of Focusing on Air Pollution Instead of Climate Change: How Discussing Power Plant Emissions in the Context of Air Pollution, Rather than Climate Change, Influences Perceived Benefits, Costs, and Political Action for Policies to Limit Emissions

2020 ◽  
pp. 107554702098044
Author(s):  
P. Sol Hart ◽  
Lauren Feldman
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Power Plant ◽  
Environmental Impacts ◽  
Political Ideology ◽  
Political Action ◽  
Perceived Benefits ◽  
Power Plant Emissions ◽  
Plant Emissions ◽  
Benefits And Costs

This experiment examines how framing power plant emissions in terms of air pollution or climate change, and in terms of health or environmental impacts, influences perceived benefits and costs of policies to reduce emissions and intentions to take political action that supports such policies. A moderated-mediation model reveals that focusing on air pollution, instead of climate change, has a positive significant indirect influence on intended political action through the serial mediators of perceived benefits and costs. Political ideology moderates the association between perceived benefits and political action. No framing effects are observed in the comparison between health and environmental impacts.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

scholarly journals Regulatory control of vehicle and power plant emissions: how effective and at what cost?

2014 ◽  
Vol 15 (4) ◽  
pp. 438-457 ◽  
Author(s):  
Sergey Paltsev ◽  
Valerie Karplus ◽  
Henry Chen ◽  
Ioanna Karkatsouli ◽  
John Reilly ◽  
...  
Keyword(s):  
Power Plant ◽  
Regulatory Control ◽  
Power Plant Emissions ◽  
Plant Emissions
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