Adding control variables to a regression doesn’t make it causal unless you know the causal structure. Controlling colliders (variables influenced by both X and Y) can create spurious links, and controlling mediators can hide real effects. Examples like COVID voluntary datasets and college-only samples show how selection turns 'controls' into bias.
— It tells readers and editors to demand causal diagrams or stated assumptions before accepting 'controlled for everything' findings as policy-relevant truth.
2025.10.07
72% relevant
The study explicitly warns that pleiotropy (shared genetic influences) can create omitted-variable bias in estimates of education’s causal effects on health; their GCTA bivariate results show common genetic factors link education with depression and self‑rated health but not BMI, illustrating why causal structure matters beyond adding controls.
Tobias Peter
2025.09.22
80% relevant
The article argues Brookings/PAVE‑aligned analyses overstated racial undervaluation by failing to control for income, education, marriage, credit, and wealth, and notes a court excluded an expert’s analysis—exactly the kind of causal‑inference critique that the 'Controls Need Causal Maps' idea urges before making policy.
Alex Tabarrok
2025.09.06
78% relevant
Eshaghnia replicates Chetty–Hendren’s model with an outcome that moves cannot affect (birth length) and still finds 'effects,' implying the specification is picking up selection/compositional structure rather than causal exposure; this is exactly the kind of collider/selection bias risk that requires an explicit causal diagram, not just 'controls.'
Isegoria
2025.09.01
70% relevant
IIHS’s Harkey says 'vehicle cost remains a factor,' while the author asks 'Vehicle cost — or driver income?', pointing to confounding between car price, driver demographics, and risk‑taking. This echoes the need to specify causal structure before attributing safety differences to vehicle attributes or marketing.
Ethan Siegel
2025.08.29
72% relevant
The article argues DESI’s apparent 'evolving dark energy' signal arises from assuming the discrepancy must live in w(z); i.e., significance is conditional on a specified model structure. That mirrors the warning that 'controlling' (or modeling) without the right causal map can create spurious inferences.
Sebastian Jensen
2025.08.27
85% relevant
He followed standard regression hygiene (transformations, p‑values, R^2/BIC, controls for 1937 GDP and years under communism) yet reached a false inference because the causal structure was misread (Eastern socialism driven by Soviet conquest, not local Jewish share), exemplifying how controlling the wrong things misleads.
Tyler Cowen
2025.08.24
55% relevant
The paper explicitly decomposes drivers of debt/GDP decline and shows the naive growth-only explanation is confounded by distorted real rates from inflation and the pre‑1951 peg, illustrating why causal structure (not just correlations) is needed for policy claims.
Tyler Cowen
2025.08.23
70% relevant
Chetty, Deming, and Friedman use idiosyncratic variation among waitlisted applicants to estimate the causal impact of Ivy-Plus attendance on elite outcomes, exemplifying why credible identification is needed rather than 'controlled for everything' associations.
Arnold Kling
2025.08.21
70% relevant
Kling argues the Agglomerations piece treats distressed ZIP codes as causing resident outcomes rather than reflecting who sorts into them, and notes age composition as a confounder—an explicit warning about causal direction and omitted variables.
Robin Hanson
2025.08.21
40% relevant
Hanson urges an explicit, staged model—moving from categorized evidence to inferred causal features (abilities, motives) before deciding actions—echoing the call to ground conclusions in clear causal structures rather than piling on 'more controls' or more anecdotes.
Davide Piffer
2025.08.20
85% relevant
By decomposing within- vs between-country nutrition effects and then adding a country-mapped height polygenic score, the analysis shows that nutrition-only regressions misattribute cross-country height differences; introducing genetic structure clarifies the causal story.
D. Paul Sullins
2025.08.20
70% relevant
The article centers on how researcher processing choices (variable definitions, outlier handling) can swing results, and highlights Young and Cumberworth’s multiverse analysis as a systematic way to expose these choices—directly aligned with the call to make causal structure and analytic assumptions explicit.
Matthew Yglesias
2025.08.19
40% relevant
Attributing mothers’ labor-force exit to return-to-office or 'Ken‑ergy' without situating it in the business cycle risks a causal mistake; the piece urges a macro-demand first model (e.g., tariffs and shocks) before invoking culture as the cause.
Pablo Arriagada
2025.08.11
60% relevant
The article separates two mechanisms—income distribution changes vs. threshold changes—to avoid misattributing a poverty jump to economic decline, mirroring the call to map causal structure before drawing policy conclusions.
Steve Stewart-Williams
2025.08.09
63% relevant
Oeberst and Imhoff propose a causal backbone for dozens of named biases—'fundamental beliefs + confirmation bias'—analogous to insisting on causal diagrams instead of piling on controls; both argue structure beats lists in explaining complex phenomena.
Cremieux
2025.07.22
100% relevant
The post’s DAG-based walkthrough and examples (COVID opt-in data; conscientiousness–career success with education as mediator/confounder) illustrate how naive controls mislead.
Adam Mastroianni
2025.05.13
63% relevant
Both argue that research must be grounded in an explicit structure of how things relate—Mastroianni calls it specifying the 'units and rules' of the system, akin to a board game, which parallels the call for causal diagrams before trusting 'controlled' findings.
José Duarte
2025.03.17
70% relevant
The article attacks Everytown’s headline correlation between its Gun Law Strength index and 'gun deaths' for having no controls or causal structure and for lumping suicides and police shootings into 'gun violence,' aligning with the call to demand causal diagrams and proper identification before treating 'controlled' results as policy-relevant.
Dr. Nathanial Bork
2025.03.08
85% relevant
The article criticizes Van Bavel et al. (2024) and Montez (2020) for attributing life‑expectancy gaps to liberal policies without properly modeling causal structure or key confounders like race composition (e.g., Mississippi 38% Black vs New York 15%), exactly the problem of 'controlling for everything' without a causal diagram.
José Duarte
2025.02.14
80% relevant
Van Bavel and Knowles assert that liberal policies cause longer life expectancy while sourcing a two‑state table (Montez 2020) and failing to control for obvious confounders like race; the article argues this is invalid causal inference without a stated causal structure.
2020.06.08
62% relevant
The paper’s conclusions hinge on structural assumptions—fixed IFR, no importation, immediate step-changes in Rt tied to intervention dates, and partial pooling across countries—illustrating how model architecture, not just 'controls,' can drive causal claims about policy effects.