Naimi, Ashley I.Rudolph, Jacqueline E.Kennedy, Edward H.Cartus, AbigailKirkpatrick, Sharon I.Haas, David M.Simhan, HyagrivBodnar, Lisa M.2024-03-202024-03-202021Naimi AI, Rudolph JE, Kennedy EH, et al. Incremental Propensity Score Effects for Time-fixed Exposures. Epidemiology. 2021;32(2):202-208. doi:10.1097/EDE.0000000000001315https://hdl.handle.net/1805/39356When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect of interest, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (incremental PS) approach to quantify the effect of shifting each person’s exposure propensity by some pre-determined amount. Compared to the ATE, incremental PS may better reflect the impact of certain policy interventions, and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relation between total vegetable intake and the risk of preeclampsia, and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least 1/2 a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman’s exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental propensity score effects in addressing public health questions with fewer assumptions.en-USPublisher PolicyMachine learningNonparametric methodsDouble-robust estimationCausal inferenceEpidemiologic methodsArticle