Propensity score plays a central role in causal inference, but its use is not limited to causal comparisons. As a covariate balancing tool, propensity score can be used for controlled descriptive comparisons between groups whose memberships are not manipulable. A prominent example is racial disparities in health care. However, conceptual confusion and hesitation persists for using propensity score in racial disparities studies. In this commentary, we argue that propensity score, possibly combined with other methods, is an effective tool for racial disparities analysis. We describe relevant estimands, target population, and assumptions. In particular, we clarify that a controlled descriptive comparisons require weaker assumptions than a causal comparison. We discuss three common propensity score weighting strategies: overlap weighting, inverse probability weighting and average treatment effect for treated weighting. We further describe how to combine weighting with the rank-and-replace adjustment method to produce racial disparity estimates concordant to the Institute of Medicine's definition. The method is illustrated by a re-analysis of the Medical Expenditure Panel Survey data.
Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common pre-specified target population. The class of balancing weights include several existing approaches such as the inverse prob…
Babies born with low and very low birthweights -- i.e., birthweights below 2,500 and 1,500 grams, respectively -- have an increased risk of complications compared to other babies, and the proportion of babies with a low birthweight is a common metric used when evaluating public health in a population. While many factors increase the risk of a baby having a low birthweight, many can be linked to the mother's socioeconomic status, which in turn contributes to large racial disparities in the incidence of low weight births. Here, we employ Bayesian statistical models to analyze the proportion of b…
The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether disparities in BMI is partially responsible for obser…
In New York City, neighborhood policing was adopted at the police precinct level over the years 2015-2018, and it is of interest to both (1) evaluate the impact of the policy, and (2) understand what types of communities are most impacted by the policy, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. We find that neighborhood policing decreases discretionary arrests in certain areas of the city, but has little effect on crime or…
Path-specific effects for pulse-oximetry guided decisions in critical care
Columbia University（哥伦比亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mila & Université de Montréal（Mila与蒙特利尔大学）；Google DeepMind（谷歌DeepMind）；Purdue University（普渡大学）
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
Columbia University（哥伦比亚大学）；Flinders University（弗林德斯大学）；University of Melbourne（墨尔本大学）；Technical University of Munich（慕尼黑工业大学）；Prince Charles Hospital Brisbane（布里斯班查尔斯王子医院）；Edith Cowan University（伊迪丝·考恩大学）；Monash University（莫纳什大学）；Massachusetts Institute of Technology（麻省理工学院）
There has been considerable interest in using decomposition methods in epidemiology (mediation analysis) and economics (Oaxaca-Blinder decomposition) to understand how health disparities arise and how they might change upon intervention. It has not been clear when estimates from the Oaxaca-Blinder decomposition can be interpreted causally because its implementation does not explicitly address potential confounding of target variables. While mediation analysis does explicitly adjust for confounders of target variables, it does so in a way that entails equalizing confounders across racial groups…
Causal Decomposition Analysis with Synergistic Interventions: A Triply-Robust Machine Learning Approach to Addressing Multiple Dimensions of Social Disparities
