Estimating Potential Outcome Distributions with Collaborating Causal Networks.

Tianhui Zhou, William E Carson, David Carlson

Abstract read

In one paragraph

Article in Transactions on machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tianhui ZhouDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, U.S.

William E CarsonDepartment of Biomedical Engineering, Duke University, Durham, NC 27705, U.S.

David CarlsonDepartment of Civil and Environmental Engineering, Department of Biostatistics and Bioinformatics, Department of Computer Science, Department of Electrical and Computer Engineering, Duke University, Durham, NC 27705, U.S.

Funding

Uncovering Population-Level Cellular Relationships to Behavior via Mesoscale NetworksR01EB026937 · NIBIB · DUKE UNIVERSITY · PI CARLSON, DAVID E · 2019 to 2019

$1.1M

NIBIB NIH HHS R01 EB026937

6 · The paper itself

Abstract

Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the

Identifiers

PMID38187355

PMCPMC10769464

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.