pith:26RXUZKT
Stacked Triple Differences
A linear regression on stacked four-cell triple-difference data identifies a cell-size-weighted average of treatment effects.
arxiv:2604.22982 v2 · 2026-04-24 · econ.EM
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Claims
I prove that, at each post-treatment event-time, a linear regression with fully saturated fixed-effects applied to the stacked dataset identifies a strictly positive, cell-size-weighted average of stack-level conditional average treatment effects, with stack weights proportional to stack-level cell sizes.
The assumption that clean comparison cohorts satisfy pairwise parallel trends with treated cohorts within each stack, and that treatment-ineligible units provide valid counterfactuals without anticipation effects or spillovers.
Stacked DDD appends self-contained stacks of treated and clean comparison cohorts to identify a cell-size-weighted average of stack-level conditional average treatment effects via saturated fixed-effects regression.
Receipt and verification
| First computed | 2026-05-20T00:04:32.622677Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d7a37a655303f8b9ce2307490d348224a0349c9735f9cba9fefbc0a678d2ffac
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/26RXUZKTAP4LTTRDA5EQ2NECES \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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