Abstract:
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Randomized controlled trials (RCTs) are increasingly prevalent in education research, as they provide an unbiased estimate of an intervention's causal impact. However, RCT sample sizes are often small, leading to low precision; in many cases RCT estimates may be too imprecise to guide policy or inform science. Observational studies have strengths and weaknesses complementary to those of RCTs, typically offering much larger sample sizes but possibly suffering from confounding. In many contexts, experimental and observational data exist side by side, and recent work has sought to integrate "big observational data" with "small but high-quality experimental data" to get the best of both. I will discuss a framework that allows researchers to employ machine learning algorithms to learn from observational data, and use the resulting models to improve precision in randomized experiments. Importantly, there is no requirement that the machine learning models are "correct" in any sense, and the final experimental results are guaranteed to be exactly unbiased. Thus, there is no danger of confounding biases in the observational data "leaking" into the experiment.
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