Paper Citation Record · LEDGER

CausalML: Python Package for Causal Machine Learning

As of 31 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2002.11631.

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

pith.paper-citation-record.v1
2002.11631 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-31T06:34:12.847434+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T06:18:50.063428Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:39.788879Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e3aefcfc-3aa0-4909-820d-c71a582b3fea · inbound

Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes cites this paper.

Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes CausalML: Python Package for Causal Machine Learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:38:32.650800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation 3fdd9e1c-624c-47da-bd7e-047edcfb53e0 · inbound

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models cites this paper.

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models CausalML: Python Package for Causal Machine Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:12:28.509131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation ef956bc0-232d-4db8-970c-14d0adb4777f · inbound

Closing the Loop: A Software Framework for AI to Support Business Decision Making cites this paper.

Closing the Loop: A Software Framework for AI to Support Business Decision Making CausalML: Python Package for Causal Machine Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:06:24.365021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation 4da0c2ef-a35e-444f-9daf-04e39c7f52ff · inbound

Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark cites this paper.

Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark CausalML: Python Package for Causal Machine Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:11:18.431065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation b7f8793d-8ebe-4aed-b7bc-f68e06428b9b · inbound

Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why cites this paper.

Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why CausalML: Python Package for Causal Machine Learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:31:30.464262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation 5d101d6d-b2cb-4c93-a4f9-7b2d695bb436 · inbound

Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation cites this paper.

Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation CausalML: Python Package for Causal Machine Learning

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:26.213323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation e7d77dba-5d6c-484f-b0e6-63b24cc7a104 · inbound

ConfoundingSHAP: Quantifying confounding strength in causal inference cites this paper.

ConfoundingSHAP: Quantifying confounding strength in causal inference CausalML: Python Package for Causal Machine Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:26:19.721959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation 00241179-e75b-4f85-abf0-0ed9e83de9ad · inbound

Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace cites this paper.

Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace CausalML: Python Package for Causal Machine Learning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:45:39.790211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.