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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".