To Keep Or NOt Keep Race Variable: AI MOdel Weighs In On Williams vs Semanaz Et Al's Debate

The Placeholder and the Poison Pill: Weighing Statistical Pragmatism Against Scientific Rigor in the Race-as-Variable Debate

By Google Studio (Gemini 2.5 Pro)

In the world of quantitative analysis, a variable is a tool. We expect it to be well-defined, reliable, and valid for its purpose. Our recent data simulations provided a sterile, controlled environment to test this expectation. We learned that when a variable (category) is designed to be conceptually invalid—a random label with no connection to the outcome—a properly specified regression model correctly identifies it as statistically insignificant noise. We also learned that degrading this variable with measurement error (misclassification) does not make it meaningful; it only adds to the statistical static. These exercises affirm a foundational principle: our tools work when the inputs are understood.

This clean, empirical lesson serves as a powerful yet unsettling prologue to the complex and contentious debate embodied by the Semanaz et al. study, Considerations When Accounting for Race and Ethnicity in Studies of Poverty and Neurodevelopment, and the subsequent critique by Dr. Christopher Williams. The Semanaz paper represents a pragmatic, if troubled, status quo, while Dr. Williams’s Critical Race Framework (CRF) and direct critique champion a more rigorous, if challenging, path forward. Weighing these positions in light of our simulation reveals that the debate is not merely about whether to "control for race," but about what we are truly doing when we let a poorly understood variable stand in for complex, unmeasured phenomena.

The Semanaz et al. Argument: A Pragmatic Case for the Necessary Placeholder

The Semanaz study is a methodologically sophisticated attempt to grapple with a difficult problem. The authors find that in their models of poverty and neurodevelopment, the inclusion of race and ethnicity variables has a statistically significant effect. Specifically, they find that race acts as both a confounder (its inclusion changes the estimated effect of poverty on some outcomes) and an effect modifier (the relationship between poverty and psychiatric outcomes differs across racial groups). Their most crucial finding, in their view, is that alternative Social Determinants of Health (SDoH) variables, while important, "could not fully account for the impact of race and ethnicity."

From this, they draw a pragmatic conclusion: because race captures a residual effect not explained by other SDoH, it is likely acting as a proxy for the unmeasured totality of structural racism. Therefore, they argue, "replacing race and ethnicity with alternative SDoH may induce bias." Their recommendation is to continue using race as a variable until more comprehensive measures of racism are developed and widely available. In this view, race is a necessary, albeit imperfect, placeholder. To remove it would be to ignore a statistically significant signal and potentially weaken the model through omitted-variable bias.

The Williams Critique: Race as a Methodological Poison Pill

Dr. Williams's critique, grounded in the principles of his Critical Race Framework, does not dispute that a statistical signal exists. Instead, he fundamentally questions its scientific meaning and the methodological rigor of the study that found it. His critique, laid bare in his email, is that the Semanaz study treats race as a scientifically meaningful variable while sidestepping the foundational work required to validate it. For Williams, the uncritical use of race is not a placeholder; it is a poison pill that invalidates the study's claims from the start.

His arguments are empirically grounded and directly attack the study’s methodology:

1. Acontextual Analysis: He points out the lack of discussion of the 21 study sites, their varying costs of living (which makes the Federal Poverty Line a weak measure), and their unique "public health economies."

2. Lack of Measurement Validity: He questions the validity of the study's own tools, such as the BPM's item redundancy and the undisclosed methods for measuring cortical surface area.

3. Race Essentialism and Ethical Concerns: By concluding that "race adds to the model" without being able to define what race is measuring, Williams argues the study defaults to a form of race essentialism. It treats "Black non-Hispanic" as a monolithic, biological-like category, which he describes as "deeply ideological and highly attenuated." He contends that "Race is a kind of placeholder for under-exploration on the part of the research team."

Synthesizing the Debate with Our Simulation's Empirical Lessons

Our data simulation provides a unique, objective lens through which to evaluate this debate. It allows us to see how a model behaves when we know the ground truth of a variable.

First, our simulation confirms that statistical significance is not the same as conceptual validity. The Semanaz study’s core defense is the significant p-value associated with the race variable. However, our exercise shows that a model can find a "signal" without the variable itself having any coherent meaning. Williams’s critique asks the essential next question: a signal of what? The Semanaz team posits it is a proxy for racism, but this is an inference made after the fact. Without a pre-defined theoretical framework for how "race" measures racism, the variable remains a black box. Our simulation of an invalid category variable showed that the model correctly found it insignificant. The Semanaz study, by contrast, finds significance but cannot fully articulate the nature of the predictor, leaving it, as Williams notes, as a "placeholder for under-exploration."

Second, the simulation highlights the danger of flawed proxies. Semanaz et al. argue that race is a necessary proxy. But our simulation showed that using a flawed (misclassified) and invalid variable introduces noise and degrades model fit. If we accept the premise that "race" is a crude, noisy proxy for the true causal construct of "racism," then the Semanaz model is, by definition, producing a biased and attenuated estimate of the effect of racism. It finds a signal, but it cannot be confident that it is the right signal or the right magnitude. This lends credence to Williams’s claim that the science is "highly attenuated."

Finally, the acontextual nature of the Semanaz study is its greatest weakness, a flaw our simulation helps clarify. Our simulation was deliberately acontextual; the numbers had no real-world meaning. This is acceptable for a mechanical test but, as Williams argues, it is unacceptable for public health research. To group individuals from 21 distinct US sites into a single category like "Hispanic/Latino" and assume they share a common variance in their experience of poverty, neurodevelopment, and racism is a profound methodological leap. It ignores the unique social, economic, and political contexts that give those variables meaning. The study’s findings of "interaction" between race and poverty are thus difficult to interpret. Is it a true interaction, or is it an artifact of averaging the experiences of vastly different communities under a single, poorly-defined categorical umbrella?

Conclusion: Beyond the Placeholder

The debate between Semanaz et al. and Dr. Williams is a debate between statistical pragmatism and scientific rigor. The Semanaz team identifies a persistent statistical shadow and argues we must keep it in our models lest we miss something vital. Dr. Williams argues that by focusing on the shadow, we fail to investigate the object casting it, and in doing so, we risk legitimizing the shadow itself as a causal force.

Our simulation forces us to side with the call for greater rigor. While removing a statistically significant variable feels like a violation of practice, including a conceptually invalid one is a violation of science. The path forward is not to simply drop race from our models, which could indeed introduce other biases. The path forward is to adopt the discipline demanded by the Critical Race Framework:

1. Acknowledge the Signal, But Challenge its Meaning: When a racial variable is significant, the work is not over; it has just begun. The researcher is now obligated to theorize and investigate why that correlation exists.

2. Demand Transparency: Methodological choices, from sampling strategies to the psychometric properties of survey tools and the context of study sites, must be explicitly discussed as they are the very factors that could explain the observed statistical effect.

3. Invest in Better Measures: The ultimate goal, which both sides would likely agree on, is to move beyond the crude placeholder of race. This requires developing and implementing direct, validated measures of systemic racism, discrimination, and other social determinants so we can finally model the true causal factors impacting health.

Until then, treating race as a simple confounding variable is an act of "under-exploration." It mistakes a statistical artifact for a scientific answer, and in a field as critical as public health, the difference between the two can have profound and lasting consequences.