Some approaches to the quantitative studies of racial health disparities, discrimination, and algorithmic fairness have involved, controversially, estimating “causal effects of race.” We approach the debate over “causal effects of race” from a social constructionist perspective. We summarize some social constructionist interpretations of race from the philosophical literature and argue that these accounts imply an individual’s race is at least “in principle” manipulable, i.e., it is conceptually coherent to posit counterfactuals about a person’s race without risking essentialism or debunked biological thinking. Thus we have a theoretical basis for interpreting and investigating the causal effects of (socially constructed) race. We further propose that quantifying causal effects of race may be indirectly relevant to anti-racist health policy and public health. Specifically, estimating the causal effects of race may be a useful starting point for inquiry into the plural mechanisms of racism, i.e., the various pathways by which racial disparities arise. This may inform policies that intervene on the mechanisms themselves.
Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the policy under such constraints. Here, we instead suggest that fairness can be directly analyzed as a property of the utility function. Instead of imposing fairness constraints on the policy, we suggest to simply maximize a utility function satisfying certain fairness properties. Concretely, we define value of information fairness, which prescribes that there must not be an incentive to infer the protected attribute. This principle suggests modifying utility functions such that they satisfy value of information fairness. We describe how such modifications can be achieved and discuss consequences for the corresponding optimal policies. We apply our framework to thought experiments and the COMPAS data, demonstrating that focusing on utility functions sometimes provides answers that better align with intuitive judgments about what is fair. Moreover, we are not aware of any intuitively fair policy that violates value of information fairness; and when we find that value of information fairness recommends an intuitively unfair policy, no realizable policy is intuitively fair.
Time-varying treatments can be confounded by time-varying prognostic factors influenced by earlier treatments. Structural nested mean models (SNMMs) adjust for time-varying confounders by parameterizing causal effects conditioned on variable history, and their parameters can be estimated using doubly robust g-estimation. For binary outcomes, log-linear SNMM parameters may be variation dependent on nuisance parameters due to probability range constraints. If the fitted model ignores this restriction, the predicted probabilities may exceed one, introducing variability into g-estimation functions. For time-fixed treatments, a nuisance parameterization for the confounder-conditional log odds product, which is variation independent of the causal parameters, provides a bivariate mapping to outcome regressions under binary treatments. The existing extension for time-varying treatments ensures all parameters constituting fully conditional regressions are variation independent of each other but requires a monotonicity assumption on the regressions when handling continuous time-varying covariates. When the interest lies in SNMM parameters and the objective is reducing variability of g-estimators, SNMM parameters may not necessarily be variation independent of all nuisance parameters across time points. We propose specifying the log odds product of SNMMs under two hypothetical treatments, avoiding the monotonicity assumption. Simulations with unbounded confounders and an application study demonstrate the improved efficiency of the g-estimators.
When making causal inferences from observational data, researchers must consider the effects of confounding. In a regression discontinuity design (RDD), individuals receive a treatment based on whether they score below or above a threshold value measured on a continuous variable. By assuming continuous regression lines for the potential outcomes at the threshold, RDD methods remove the confounding bias in estimating the treatment effect at the threshold. This effect is estimated by the jump in the regression line for the observed outcome at the threshold. Although RDD methods have received deserved attention in economics, the social sciences, and epidemiology, we show that inferences from RDDs using local and global linear regression estimators are prone to regression to the mean bias in certain situations. A common situation where the bias occurs is when a running variable has a normal distribution and the cutoff is relatively far from the mean of this distribution. We derive the expression for the limiting bias in this case. In general, the bias occurs when some units receive (or do not receive) treatment when their running variable values are extreme relative to the typical value of the running variable. Through simulations, we show that the regression to the mean bias can lead to inflated type I error rates and bias toward the null in typical settings. Simulations show that the RTM effect can be different for different estimators. We develop a novel method to correct this bias and provide valid inferences. We verify our correction method in simulations and apply it to a real-life example of the incumbency advantage in U.S. House elections.
Vaccine safety surveillance programs that monitor possible short-term rare adverse events following vaccination usually only have access to data on vaccinated individuals who experienced the event of interest. The Self-Controlled Case Series design employs such data and compares the risk of the event in an “risky” period immediately after vaccination to that in a “baseline risk” period where the transient risk should be gone. To ensure valid analysis, some assumptions have been given in the literature while others are made implicitly through parametric modeling. In this work, we provide a complete formal causal framework for the Self-Controlled Cases Series design. We provide sufficient conditions for a causal interpretation of the contrasts estimated from the data on exposed individuals who experienced the event. These conditions are intuitive but often cannot be tested from the available data. We describe practical settings where these conditions may be violated. When the conditions are not met, the contrasts estimated in practice do not clearly relate to any quantities of causal interest, and should therefore be interpreted with caution.
