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Quantifying causality in data science with quasi-experiments


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.

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Fig. 1: Graphical representations for estimating causality in experimental and observational data.
Fig. 2: DAG and plots of IVs.
Fig. 3: DAG and plots of RDDs.
Fig. 4: DAG and plots of DiD.

Code availability

We provide interactive widgets of Figs. 24 in a Jupyter Notebook hosted in a public GitHub repository ( and served through Binder (see link in the GitHub repository).


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We thank R. Ladhania and B. Lansdell for their comments and suggestions on this work. We acknowledge support from National Institutes of Health grant R01-EB028162. T.L. is supported by National Institute of Mental Health grant R01-MH111610.

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T.L. helped write and prepare the manuscript. L.U. and K.K. jointly supervised this work and helped write the manuscript. All authors discussed the structure and direction of the manuscript throughout its development.

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Correspondence to Konrad Kording.

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Peer review information Fernando Chirigati was the primary editor on this Perspective and managed its editorial process and peer review in collaboration with the rest of the editorial team. Nature Computational Science thanks Jesper Tegnér and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Liu, T., Ungar, L. & Kording, K. Quantifying causality in data science with quasi-experiments. Nat Comput Sci 1, 24–32 (2021).

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