The Beneficial Learning Effects of Combining a Hypothesis-Testing Mindset with a Causal Model
Bibliographic record
Abstract
ABSTRACT Firms often use causal models to align decision-making with strategic objectives. However, firms often operate in changing environments such that an accurate causal model can become inaccurate. Prior research has not examined the consequences that a change in the accuracy of causal models may have for managerial learning. Using an experiment, we predict and find that providing an accurate causal model positively affects managerial learning, and this positive effect is not reduced by encouraging a hypothesis-testing mindset (HTM). However, when the model subsequently becomes inaccurate, we predict and observe that providing a causal model alone negatively affects managerial learning, although this effect is partially mitigated by additionally encouraging a HTM. Our results can inform designers of control systems about the potential implications of providing a causal model when its accuracy changes over time and demonstrate how simple encouragement of a HTM moderates the effects of providing a causal model. Data Availability: Contact the authors. JEL Classifications: C91; M41.
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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 | 0.037 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".