Causal Inference in Social Science An Elementary Introduction
نویسنده
چکیده
This is a short and very elementary introduction to causal inference in social science applications targeted to machine learners. I illustrate the techniques described with examples chosen from the economics and marketing literature. 1 A motivating problem 1 Suppose you are given some data on ad spend and product sales in various 2 cities and are asked to predict how sales would respond to a contemplated 3 change in ad spend. If yc denotes per capita sales in city c and xc denotes 4 per capita ad spend in city c it is tempting to run a regression of the form 5 yc = bxc+ec where ec is an error term and b is the coefficient of interest. 1 (The 6 machine learning textbook by James et al. [2013] that describes a problem 7 of this sort on page 59.) 8 Such a regression is unlikely to provide a satisfactory estimate of the 9 causal effect of ad spend on sales. To see why, suppose that the sales, yc, are 10 per capita box office receipts for a movie about surfing and xc are per capita 11 TV ads for that movie. There are only two cities in the data set: Honolulu, 12 Hawaii and Fargo, North Dakota. 13 We assume all data has been centered, so we can ignore the constant in the regression.
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