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Mathematics

Causal Inference from Observational Data Using Counterfactuals

Quick fact

Observational data, like health records or social media activity, can be used to estimate causal effects without ever running a randomized experiment—by carefully comparing what actually happened to what we predict would have happened in an alternative scenario, a 'counterfactual'.

Why this is interesting

You've probably heard that 'correlation does not imply causation.' But how can we ever know what causes what, if we can't run an experiment on everything? The answer lies in asking a clever question about a world that never happened.

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Causal Inference from Observational Data Using Counterfactuals

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