Causal Inference and Understanding Causal Structure
نویسنده
چکیده
Acknowledgements I would like to give special thanks to my advisor, Professor Kevin Hoover who took my shortcomings and successes with equal stride, and who struggled through the process alongside me. But how will you look for something when you don't in the least know what it is? How on earth are you going to set up something you don't know as the object of your search? To put it another way, even if you come right up against it, how will you know that what you have found is the thing you didn't know?-Plato's Meno (80D) 3 Abstract This thesis aims to show that explicit understanding of possible causal structures often aids in inferring the true causes from data. This is done by first understanding that causes are chains of counterfactual dependence. Insofar as experiments, active or natural are not perfect, data can easily support false counterfactuals. Even those tools especially designed to identify unbiased estimates, like instrumental variables, often fail. Causal structure explains the failure of these tools, but more importantly allows us to better identify which counterfactuals to reject or accept.
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