ArticleProceedings of machine learning research2019
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects.
Article in Proceedings of machine learning research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Simple graphical rules for assessing selection bias in general-population and selected-sample treatment effects.American journal of epidemiology · 2025Article
- The interventionist approach can address questions related to causes of effects if causes are considered as states instead of interventions.Observational studies · 2025Article
- Mediated probabilities of causation.Journal of causal inference · 2025Article
- The Causal Fairness Field Guide: Perspectives From Social and Formal Sciences.Frontiers in big data · 2022Review
- Deriving Bounds and Inequality Constraints Using Logical Relations Among Counterfactuals.Proceedings of machine learning research · 2020Article
- Identification and Estimation of Causal Effects Defined by Shift Interventions.Proceedings of machine learning research · 2020Article
- Identification in Causal Models With Hidden Variables.Journal de la Societe francaise de statistique (2009) · 2020Article
- Intervening on Network Ties.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference [1, 8, 18]. Nevertheless, as it is currently defined, the do-calculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of path-specific effects and dynamic treatment regimes. In this paper we present the
Identifiers
31886462PMC6935349What OpenQuestion holds
Registered trials
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.