Causal inference with observational data
Causal inference, Causality, Homophily and influence
Many methods aim to emulate controlled experiments.
Matching (statistics) methods try to approximate Randomized controlled trial with blocked assignments, assuming that the treatment is exogenous. There are fundamental challenges such as Balance sample size frontier.
Can bigger data help causal inference? Titiunik2014can points out that the actual science doesn’t change much with big data and big data by itself does not necessarily address the challenges of Causal inference. Hernán2016using suggests ways to emulate a target trial using big data.
But Machine learning holds great potential, although Machine learning does not automatically solve causal inference problem. Lots of data means we can learn better predictive models, which can then be used to produce counterfactuals (see Varian2016causal and Prosperi2020causal). Or, the Representation learning can also help by providing the ability to represent entities (e.g., patients) with dense vectors.
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