ArticleAmerican journal of epidemiology2021
Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled 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.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Machine learning in causal inference for epidemiology.European journal of epidemiology · 2024Article
- Combining artificial neural networks and a marginal structural model to predict the progression from depression to Alzheimer's disease.Frontiers in dementia · 2024Article
- Defining and estimating effects in cluster randomized trials: A methods comparison.Statistics in medicine · 2023Review
- Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this issue of the Journal, Mooney et al. (Am J Epidemiol. 2021;190(8):1476-1482) discuss machine learning as a tool for causal research in the style of Internet headlines. Here we comment by adapting famous literary quotations, including the one in our title (from "Sonnet 43" by Elizabeth Barrett Browning (Sonnets From the Portuguese, Adelaide Hanscom Leeson, 1850)). We emphasize that any use of machine learning to answer causal questions must be founded on a formal framework for both causal and statistical inference. We illustrate the pitfalls that can occur without such a foundation. We conclude with some practical recommendations for integrating machine learning into causal analyses in a principled way and highlight important areas of ongoing work.
Indexed as
Identifiers
33751059What 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.