Evaluating Public Health Interventions
8. Causal Inference for Time-Invariant Interventions
Bibliographic Data
| ID | 11031701 |
|---|---|
| Authors | Donna Spiegelman (0000-0003-4006-4650, Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Xin Zhou is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health.), Xin Zhou (0000-0003-2640-6431, Donna Spiegelman is with the departments of Epidemiology, Biostatistics, Nutrition, and Global Health, Harvard T. H. Chan School of Public Health, Boston, MA. Xin Zhou is with the departments of Epidemiology and Biostatistics, Harvard T. H. Chan School of Public Health.) |
| Year | 2018 |
| Volume | 108 |
| Issue | 9 |
| Pages | 1187-1190 |
| Publication date | 2018-09-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | American Journal of Public Health (JOURNAL) |
| Journal identifiers | ISSN: 0090-0036 • E-ISSN: 1541-0048 |
| Publisher | American Public Health Association (PUBLISHER • US) |
| DOI | 10.2105/ajph.2018.304530 |
| PMID | 30024804 |
| OpenAlex | W2883925922 |
| Language | EN |
| Citations received | 2 |
| References cited | 16 |
We provide an overview of classical and newer methods for the control of confounding of time-invariant interventions to permit causal inference in public health evaluations. We estimated the causal effect of gender on all-cause mortality in a large HIV care and treatment program supported by the President’s Emergency Program for AIDS Relief in Dar es Salaam, Tanzania, between 2004 and 2012. We compared results from multivariable modeling, three propensity score methods, inverse-probability weighting, doubly robust methods, and targeted maximum likelihood estimation. Considerable confounding was evident, and, as expected by theory, all methods considered gave the same result, a statistically significant approximately 20% increased mortality rate in men. In general, there is no clear advantage of any of these methods for causal inference over classical multivariable modeling, from the point of view of either bias reduction or efficiency. Rather, given sufficient data to adequately fit the multivariable model to the data, multivariable modeling will yield causal estimates with the greatest statistical efficiency. All methods can adjust only for well-measured confounders—if there are unmeasured or poorly measured confounders, none of these methods will yield causal estimates
Causal inference · Confounding · Econometrics · Inverse probability weighting · Marginal structural model · Multivariable calculus · Point estimation · Propensity score matching · Psychiatry · Psychological intervention · Statistical inference · Statistics · Weighting · Advanced Causal Inference Techniques · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Statistical Methods and Inference
Doubly Robust Estimation in Missing Data and Causal Inference Models
Doubly Robust Estimation of Causal Effects
Variable Selection for Propensity Score Models
Stratification and weighting via the propensity score in estimation of causal treatment effects
Evaluating Public Health Interventions
Evaluating Public Health Interventions
| Citations per year | 0.29 |
|---|---|
| Citation span | 2019 - 2020 (2) |
| Highly cited | No |