Lars P. Syll

Some methodological perspectives on causal modeling in economics Causal modeling attempts to maintain this deductive focus within imperfect research by deriving models for observed associations from more elaborate causal (‘structural’) models with randomized inputs … But in the world of risk assessment … the causal-inference process cannot rely solely on deductions from models or other purely algorithmic approaches. Instead, when randomization is...

What kind of evidence do RCTs provide? Perhaps it is supposed that the assumptions for an RCT are generally more often met (or meetable) than those for other methods. What justifies that? Especially given that the easiest assumption to feel secure about for RCTs—that the assignment is done “randomly”—is far from enough to support orthogonality, which is itself only one among the assumptions that need support. I sometimes hear, “Only the RCT can control for...

Sex and the problem with interventionist definitions of causation We suggest that “causation” is not univocal. There is a counterfactual/interventionist notion of causation—of use when one is designing a public policy to intervene and solve a problem—and an historical, or more exactly, etiological notion—often of use when one is identifying a problem to solve … Consider sex: Susan did not get the job she applied for because the prejudiced employer took her...

Was the Swedish corona strategy a success? .[embedded content] Any scientific discussion about whether all or some versions of treatment lead to the same causal conclusion rests, again, on expert consensus and judgement. Because experts are fallible, the best we can do is to make these discussions—and our assumptions—as transparent as possible, so that others can directly challenge our arguments. Miguel Hernán

Which causal inference method is the best one? .[embedded content]

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Graphical causal models and collider bias Why would two independent variables suddenly become dependent when we condition on their common effect? To answer this question, we return again to the definition of conditioning as filtering by the value of the conditioning variable. When we condition on Z, we limit our comparisons to cases in which Z takes the same value. But remember that Z depends, for its value, on X and Y. So, when comparing cases where Z...

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Hunting for causes (wonkish) There are three fundamental differences between statistical and causal assumptions. First, statistical assumptions, even untested, are testable in principle, given sufficiently large sample and sufficiently fine measurements. Causal assumptions, in contrast, cannot be verified even in principle, unless one resorts to experimental control. This difference is especially accentuated in Bayesian analysis. Though the priors that...