omitted variable bias methods of economic investigation lecture 7 1

Omitted Variable Bias

Methods of Economic Investigation

Lecture 7

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Today’s Lecture Review Regression Framework

Linking last term to this term Understanding conditional expectations

Covariates Added and Removed Why is this “Controlling” What if you leave something out?

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Review: This Course So Far Do we know what the effect of X is on y?

Defining concepts of “treatment” and “control” Experimental settings: Explaining the

usefulness of Random Assignment Non-Experimental settings: variation is `as if’

random In Theory, all you need to do if you have

random assignment is take a difference in means

Go backwards a bit…3

General Goal in Causal Inference E(Y| T=1) – E(Y | T=0) or

E(Y| T=1, X) – E(Y | T=0, X)

So we need to know how to estimate a conditional expectation function

We’ll worry about if can be causally interpreted later—right now let’s just if we can even show how we could estimate it

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What is a conditional expectation The conditional expectation function (CEF) is

the population average of Y with X held fixed CEF =E[Yi | Xi] It is a function of X and because X is a

random variable, the CEF is itself random We have been doing the special case where

the CEF only takes 2 values E[Yi | Ti=1] and E[Yi | Ti=0] In practice, could be continuous but we’ll come back to that

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Defining the CEF For a continuous Y with a density fy(. | X=xi)

Let’s ground this in reality a bit: The expectation is a concept because we rarely

observe the entire population Generally we’re going to use data to make

inferences about the sample CEF Last term you proved under what conditions and

assumptions this sample CEF converges to the population CEF

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How is Y related to the CEF The observed outcome Y can be

decomposed into the bit that is explained by X (the CEF) and the unexplained bit ε

Then last term you learned that CEF is the “best” (in a MMSE sense) summary

of the relationship between Y and X Can also decompose the variance usefully into

explained variance and unexplained variance

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How is OLS related to this? If the true CEF is linear, it’s the sample

estimate Example joint normality Saturated model (different parameter for every

possible combination of values for X) Best Linear Predictor (BLUE)

Best we can relative to other linear estimates Common justification—local linear approximation

Minimizes Mean Squared Error Implies it is the best linear approximation to CEF

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This should be familiar You are probably used to thinking OLS as

Turns out that is the thing is the parameter we need to estimate the CEF

And the sample analog that we estimate will converge to the true β (under appropriate conditions

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How do we establish Causality? Conditional Independence Assumption

In words: on average, outcomes would be the same in the treatment and control groups

In Math: Let Y1 be potential outcome if T=1 and Y0 be potential outcome of T=0

Can write Y = Y0 + (Y1 – Y0) *T

Then CIA: {Y0i,Y1i} independent of Ti (or Ti | Xi)

Or E[Yi | T=1 ] – E[Yi| T=0] = E[Y1i – Y0i]

Intuition: since we never observe both Y0 and

Y1 for any one individual, we want to think on average, the two groups are the same

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Selection Bias The Reason we need CIA: Selection bias

In words: different people choose to be in the treatment or control group. The underlying differences in these people would generate differences in outcomes regardless of the treatment

E(Y| T=1) – E(Y1 | T=0)

=E(Y - Y0 |Ti=1) +{E(Y0 | T=1) – E(Y0 | T=0)}

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What is generating the bias? If it’s selection on observables

we can condition on X—estimate the CEF Convenient that the regression can partial this out: let’s

see how

Simple example: 2 variables, T and X, each of which are either zero or one. So, the CEF can have 4 values E[Y| T=0, X=0]=α E[Y| T=1, X=0]= α+β E[Y| T=0, X=1]= α+γ E[Y| T=1, X=1]= α+β+γ+δ

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Extracting β from the estimated CEF Imagine we estimate each CEF’ Write a single equation for the CEFE[Y | T, X] = α + βT +γX+δ(T*X)

What is β = E[Y| T=1, X=0]- E[Y| T=0, X=0] Why is X fixed? So we can get β out regression Generalize this to a continuous X, and we just hold

it at some fixed point (e.g. it’s mean)13

Main effect of T Main effect of X Interaction

What if T didn’t vary with X? If X has some fixed association with Y

independent of T, then the CEF only has 2 values:

E[Y| T=0, X=0]=α E[Y| T=1, X=0]= α+β E[Y| T=0, X=1]= α E[Y| T=1, X=1]= α+β

[for ease we can set γ and δ to zero…] In that case—we’re ok and can exclude X.

But we can increase precision because we get 2 estimates of the CEF if we include X 14

Omitted Variable Bias Formula

Simply from before, suppose δ=0 so our true CEF is:

E[Y | T, X] = α + βT +γX(REMEMBER: This means Y = E[Y | T, X] +e) We estimate

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What is happening when we estimate? What this is doing is given observed Y’s for the

various values of T it is choosing the α and β that minimize the mean square error

intuitively, the bias happens because the values of Y’s are changing not only with respect to T but also the unobserved X

In more formal terms we get:

17True β (this is what we want)

Relationship between X and Y

Should look familiar

What is relationship between X and T Pretend we could observe X…then w could

think about estimating

Well, we know, since this is OLS and assuming no OVB in this equation that

So our omitted variable bias formula can be re-written as:

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Signing your bias Generally, the concern with OVB is that it

biases your estimate upwards.

The sign of the bias will be as follows:

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γ<0 γ>0

θ<0 positive negative

θ>0 negative positive

What did we learn today Conditional Expectation Functions (CEFs) are

how we determine “causal effects”

Regressions allow the “best” way to estimate these CEFs

Selection on observables is not something that will bias our estimates

Selection on unobservables is a problem20

Next Class Review of OVB

Error Correction Models Fixed Effects Random Effects (interaction terms)

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omitted variable bias methods of economic investigation lecture 7 1

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