Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two hospitals could be due to differences in the patients themselves (covariates) or differences in medical care (outcomes given covariates). The Oaxaca--Blinder decomposition (OBD) is a standard tool to tease apart these factors. It is well known that the OBD requires choosing one of the groups as a reference, and the numerical answer can vary with the reference. To the best of our knowledge, there has not been a systematic investigation into whether the choice of OBD reference can yield different substantive conclusions and how common this issue is. In the present paper, we give existence proofs in real and simulated data that the OBD references can yield substantively different conclusions and that these differences are not entirely driven by model misspecification or small data. We prove that substantively different conclusions occur in up to half of the parameter space, but find these discrepancies rare in the real-data analyses we study. We explain this empirical rarity by examining how realistic data-generating processes can be biased towards parameters that do not change conclusions under the OBD.

Machine learning (ML) algorithms can often differ in performance across domains.

Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants.

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Why is okra so slimy? The plant's signature goo helps it thrive in the heat. Okra gets its slime from a substance called mucilage. Breakthroughs, discoveries, and DIY tips sent six days a week. Okra is one of those vegetables with a polarizing reputation.

Automatic cocktail machines are silly, but also kind of fun. Here's how to choose between the Bartesian and Barsys devices. The machine on my kitchen table is a holy device, if your definition of "holy" is that it looks like a glowing halo and it's filled with spirits. The machine has taken up a task I consider sacred: making me a cocktail. In advance of holiday party season, I have been testing a pair of devices that promise an indulgent future, a life where machines can make you a passable Old Fashioned.

Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method.