Paper Citation Record · LEDGER

Causal inference with dyadic data in randomized experiments

As of 12 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2505.20780.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.20780 v2

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:55:14.798123Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T09:01:44.224582Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-01T21:26:14.258371Z

Reference resolution

16 of 16 outbound references displayed

verified exact0
verified fuzzy11
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bda5e5fd-9ec5-41f8-b581-0ab053c5aadf · outbound

This paper cites an unresolved cited work.

Causal inference with dyadic data in randomized experiments Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:55:17.404633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4900fe73-ba31-499a-b04f-e41779057448 · outbound

This paper cites We also match each coefficient from the above expectation withτ τ= 1 n nX i=1 βi + X j̸=i (γij +λ ij).

Causal inference with dyadic data in randomized experiments We also match each coefficient from the above expectation withτ τ= 1 n nX i=1 βi + X j̸=i (γij +λ ij)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:17.243122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1be2dba0-389e-4cb6-aaa1-39cb29193e9d · outbound

This paper cites an unresolved cited work.

Causal inference with dyadic data in randomized experiments Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:55:17.148510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2bab670b-5daa-42b1-831f-3067f69b734e · outbound

This paper cites The variance decomposition terms in (S.3) can be categorised by subscript overlap patterns: (A).

Causal inference with dyadic data in randomized experiments The variance decomposition terms in (S.3) can be categorised by subscript overlap patterns: (A)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:16.971529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7d65f9bd-3fad-4819-b8d1-a0d785102f3f · outbound

This paper cites Next, we consider each covariance term in (S.3) under complete randomization.

Causal inference with dyadic data in randomized experiments Next, we consider each covariance term in (S.3) under complete randomization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:16.744582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9cbc7956-3a32-4353-ae47-e1306ac6896b · outbound

This paper cites an unresolved cited work.

Causal inference with dyadic data in randomized experiments Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:55:16.455400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b6743e8-8e45-47c6-afd5-1a878a05db44 · outbound

This paper cites Given some fixedn1, we letT= (T 1, T2,.

Causal inference with dyadic data in randomized experiments Given some fixedn1, we letT= (T 1, T2,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:16.189535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 84d884c7-9a8f-42c7-a194-f23552353a41 · outbound

This paper cites an unresolved cited work.

Causal inference with dyadic data in randomized experiments Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:55:15.991914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1c1e847-035b-4c41-a0bb-45660f457c12 · outbound

This paper cites Combining (S.18) and (S.19), we have var 1 n nX i=1 X j̸=i ZijS2 ij 2p2 ij ! ≤   1 n2 nX i=1 |Ni(1)|2 + 1 n2 nX i=1 X j∈Ni(1) |Nj(1)|   C5K 4 =O d2(1)n−1.

Causal inference with dyadic data in randomized experiments Combining (S.18) and (S.19), we have var 1 n nX i=1 X j̸=i ZijS2 ij 2p2 ij ! ≤   1 n2 nX i=1 |Ni(1)|2 + 1 n2 nX i=1 X j∈Ni(1) |Nj(1)|   C5K 4 =O d2(1)n−1

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.886235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c256a18-a643-4b85-8e60-bb2b6983008a · outbound

This paper cites The covariance is cov ZijkSijSik pijpik , ZlmnSlmSln plmpln = 0, 50 sinceZ ijk andZ lmn are independent random variables under Bernoulli randomization.

Causal inference with dyadic data in randomized experiments The covariance is cov ZijkSijSik pijpik , ZlmnSlmSln plmpln = 0, 50 sinceZ ijk andZ lmn are independent random variables under Bernoulli randomization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.785681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1489092-350e-41a1-9f02-545964d9ef6d · outbound

This paper cites For (S.20), we have var 1 n nX i=1 X j̸=i X k̸=i k̸=j ZijkSijSik pijpik ≤ 1 n2 nX i=1 X j∈Ni(1) X k∈Ni(1)\{j} C6K 4d2 ∞(1) =O d2(1)d2 ∞(1)n−1.

Causal inference with dyadic data in randomized experiments For (S.20), we have var 1 n nX i=1 X j̸=i X k̸=i k̸=j ZijkSijSik pijpik ≤ 1 n2 nX i=1 X j∈Ni(1) X k∈Ni(1)\{j} C6K 4d2 ∞(1) =O d2(1)d2 ∞(1)n−1

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.691436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4f2e9856-515c-478a-8d1c-bda5c927dc32 · outbound

This paper cites There are PL u=1 ICu (1) 2 choices of dyads (i, j) and (k, l) in this case.

Causal inference with dyadic data in randomized experiments There are PL u=1 ICu (1) 2 choices of dyads (i, j) and (k, l) in this case

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.611300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 89f0a8c5-daec-4b7f-9ed5-ed4fa3a7f161 · outbound

This paper cites There are PL u=1 ICu(1)∂Cu(1) choices of dyads (i, j) and (k, l) in this case.

Causal inference with dyadic data in randomized experiments There are PL u=1 ICu(1)∂Cu(1) choices of dyads (i, j) and (k, l) in this case

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.407331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9c2bf4a-39e8-4327-a53c-fe822ef18a2f · outbound

This paper cites (i, k) are from the same cluster while and (j, l) are from another cluster: cov ZijYij pij , ZklYkl pkl ≤ (1−p upv) pupv ≤P −2 1.

Causal inference with dyadic data in randomized experiments (i, k) are from the same cluster while and (j, l) are from another cluster: cov ZijYij pij , ZklYkl pkl ≤ (1−p upv) pupv ≤P −2 1

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.206256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c840e20-889a-4fea-8d80-b8224196bb82 · outbound

This paper cites Case 3.(a) and 4 together have less than PL u=1 ∂Cu(1)2 choices of dyads (i, j) and (k, l) in this case.

Causal inference with dyadic data in randomized experiments Case 3.(a) and 4 together have less than PL u=1 ∂Cu(1)2 choices of dyads (i, j) and (k, l) in this case

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:55:15.067996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a2cb82da-ca01-40ac-9df6-9a0c2f79cad3 · outbound

This paper cites an unresolved cited work.

Causal inference with dyadic data in randomized experiments Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:55:14.989712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 98077eea-8e5e-4904-9567-1e77981a443c · inbound

Design-based edge-level causal inference with machine learning assisted covariate adjustment cites this paper.

Design-based edge-level causal inference with machine learning assisted covariate adjustment Causal inference with dyadic data in randomized experiments

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-01T21:26:14.259756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d219d57d-2d56-4b4b-9025-bf2ca88cbc07 · inbound

Causal Estimation of Share-Induced Engagement with Flywheel Effects cites this paper.

Causal Estimation of Share-Induced Engagement with Flywheel Effects Causal inference with dyadic data in randomized experiments

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T09:01:44.224582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.