ArticleNature computational science2021
Quantifying causality in data science with quasi-experiments.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Trial
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Causal inference concepts can guide research into the effects of climate on infectious diseases.Nature ecology & evolution · 2025Article
- Machine Learning-Based Prediction of Well Logs Guided by Rock Physics and Its Interpretation.Sensors (Basel, Switzerland) · 2025Article
- Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Inferring causal connectivity from pairwise recordings and optogenetics.PLoS computational biology · 2023Article
- Article
- Claim causality with clarity.Psychoradiology · 2023Review
- Gendered beliefs about mathematics ability transmit across generations through children's peers.Nature human behaviour · 2022Article
- Modeling multiscale causal interactions between spiking and field potential signals during behavior.Journal of neural engineering · 2022Article
- Modes of information flow in collective cohesion.Science advances · 2022Article
- The impact of unemployment benefits on birth outcomes: Quasi-experimental evidence from European linked register data.PloS one · 2022Article
- Article
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
Estimating causality from observational data is essential in many data science questions but can be a challenging task. Here we review approaches to causality that are popular in econometrics and that exploit (quasi) random variation in existing data, called quasi-experiments, and show how they can be combined with machine learning to answer causal questions within typical data science settings. We also highlight how data scientists can help advance these methods to bring causal estimation to high-dimensional data from medicine, industry and society.
Identifiers
What 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.