JIAB-2026-9JMQ
Heterogeneous Treatment Effects in Education Policy: Evidence from Causal Forests
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Editor
Sofia Reyes
Handling editor for this submission.
Assigned editor: Sofia Reyes
Update the handling editor to rebalance workload or hand off the submission.
Abstract
We estimate heterogeneous treatment effects of district-level tutoring interventions using causal forests fitted to linked administrative records. The results show substantial variation by baseline achievement and school resources, suggesting that average treatment effects mask practically important subgroup differences.
Reviews
2 itemsR1 Henry Weber Submitted Oct 28, 2026
The paper is well executed and the empirical application is strong. My main suggestion is to expand the explanation of overlap diagnostics and to clarify how the honesty constraints in the causal forest were tuned. Subject to those revisions, I would support acceptance.
R2 Kate Okonkwo Submitted Oct 30, 2026
The manuscript makes a useful contribution by showing where treatment-effect heterogeneity matters for education policy. I would like to see a slightly fuller discussion of policy transportability and a short appendix table with subgroup sample sizes, but the paper is otherwise ready for an editorial decision.
Files
1 manuscriptReviewers
2 of 2 receivedDrag names to change the reviewer order for people who have not been invited yet.
Henry Weber
Kate Okonkwo
Messages
2 messagesA Author Cover letter
We submit this manuscript as a methodological contribution with direct policy relevance. The paper combines modern heterogeneous-effect estimation with a transparent applied workflow.
E Editor Reviews received
Dear Dr. MartÃnez,
We have now received two reviews of your manuscript and the handling editor is preparing a decision. We expect to be back in touch shortly.
Best regards,
The Editors
Timeline
1 events
Waiting for Reviewers
Oct 18, 2026
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