lecture5_140b_2011

lecture5_140b_2011 - Lecture 5: Evaluating Regression...

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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Lecture 5: Evaluating Regression Studies 3 reasons to regress Causal relationships Observational studies, controlled randomized experiment, natural experiment Threats to internal and external validity Chapter 9 in S&W
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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Three reasons to regress: 1. Summarize the data (i.e. calculate M/F wage differential) 2. Forecast future values of Y (i.e. predict Urate in 2012) 3. Predict the impact of interventions / policy changes There is nothing special about (1) and (2). Just regress, fit the data as best as possible However (3) is about causal inference: what is the effect of an exogenous change in X on Y The question is when does a regression estimate a causal effect? Also when does it not?
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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Causal relationships „Causality‟ is a matter of deep philosophical questioning For our purposes, we say that an action (T) causes an outcome (Y), if Y is the direct result of that action We will call T the treatment and Y the outcome Examples of causation: Pressing the “on” button on the remote causes the TV starts Putting your hand in a fire causes your hand to get burned Counter-examples (correlation causation): Walking with an umbrella does not cause rain to fall (but it is correlated with rainfall)
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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Simple example: Y i = 0 + 1 T i + u i T i = 1(observation i receives treatment) 1 = (average) causal effect of T of Y = E[Y i |T i =1]- E[Y i |T i =0] Can we estimate the causal effect of T on Y from data? Key issue is whether the assumption E[u i |T i ]=0 is satisfied or some sort of exogeneity assumption Earlier, we simply assumed it. From now on we will always discuss if this assumption is credible
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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Causal inferences are made from: Observational studies Natural experiments Controlled experiments In economics, we almost exclusively deal with observational data With observational (non-experimental) data, a key issue is confounding (or omitted variables bias) Confounding arise if u i is correlated with T i , even after we condition on other variables (X i )
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Olivier Deschenes, UCSB, Econ 140B, Winter 2011 Controlled randomized experiment In a controlled experiment, treatment assignment is random (ex: coin toss) Up to some random error, the coin toss balances the two groups (treatment group T=1, control group T=0) with respect to all factors except the treatment This makes the assumption E[u i |T i ]=0 highly credible since T i is randomly assigned, and thus in principle independent of u i Causal inferences based on controlled experiments are the most credible However, in social sciences, controlled experiments are rare (because unethical, unpractical, expensive, etc)
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This note was uploaded on 09/04/2011 for the course ECON 140b taught by Professor Staff during the Winter '08 term at UCSB.

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lecture5_140b_2011 - Lecture 5: Evaluating Regression...

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