Bibliographic record
Abstract
The thesis of this short note is that post-intervention probabilities can be considered to be certain conditional probabilities. Acyclic causal modelsLet us consider an acyclic causal model M of the sort that is central to causal modeling (Spirtes et al. 1993/2000, Pearl 2000/2009, Halpern 2016, Hitchcock 2018).Readers familiar with them can skip this section.M = ⟨S, F ⟩ is a causal model if, and only if, S is a signature and F = {F 1 , . . ., F n } represents a set of n structural equations, for a finite natural number n. S = ⟨U, V, R⟩ is a signature if, and only if, U is a finite set of exogenous variables, V = {V 1 , . . ., V n } is a set of n endogenous variables that is disjoint from U, and R : U ∪ V → R assigns to each exogenous or endogenous variable X in U ∪ V its range (not co-domain) R (X) ⊆ R. F = {F 1 , . . ., F n } represents a set of n structural equations if, and only if, for each natural number i, 1 ≤ i ≤ n: F i is a function from the Cartesian product W i = × X∈U∪V\{V i } R (X) of the ranges of all exogenous and endogenous variables other than V i into the range R (V i ) of the endogenous variable V i .The set of possible worlds of the causal model M is defined as the Cartesian product W = × X∈U∪V R (X) of the ranges of all exogenous and endogenous variables.A causal model M is acyclic if, and only if, it is not the case that there are m endogenous variables V i1 , . . ., V im in V, for some natural number m, 2 ≤ m ≤ n, such that the value of F i(j+1) depends on R V i j for j = 1, . . ., m -1, and the value of F i1 depends on R (V im ).Importantly, dependence is just ordinary functional dependence: F i depends on R V j if, and only if, there are arguments ⃗ w i and ⃗ w i ′ in the domain W i = × X∈U∪V\{V i } R (X) of F i that differ only in the value from R V j such that their values under F i differ, F i ⃗ w i F i ⃗ w i ′ .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".