# ECON Introductory Econometrics. Lecture 17: Experiments

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1 ECON Introductory Econometrics Lecture 17: Experiments Monique de Haan Stock and Watson Chapter 13

2 Lecture outline 2 Why study experiments? The potential outcome framework. An ideal randomized experiment, potential outcomes & regression analysis Conditional mean independence vs conditional mean zero Randomized experiments in practice Threats to internal validity in a randomized experiment Threats to internal validity in a randomized experiment

3 Why study experiments? 3 Ideal randomized controlled experiments provide a conceptual benchmark for assessing observational studies. Experiments can overcome the threats to internal validity of observational studies, however they have their own threats to internal and external validity. Actual experiments are rare (\$\$\$) but influential. Thinking about experiments helps us to understand quasi-experiments, or natural experiments, if natural variation induces as if random assignment (topic of next week)

4 Terminology: experiments and quasi-experiments 4 An experiment is designed and implemented consciously by human researchers. An experiment randomly assigns subjects to treatment and control groups (think of clinical drug trials) A quasi-experiment or natural experiment has a source of randomization that is as if randomly assigned. This variation was however not the result of an explicit randomized treatment and control design. Program evaluation is the field of econometrics aimed at evaluating the effect of a program or policy, for example, an ad campaign to cut smoking, or a job training program.

5 Different Types of Experiments: Three Examples 5 Clinical drug trial: does a proposed drug lower cholesterol? Y = cholesterol level X = treatment or control group (or dose of drug) Job training program Y = has a job, or not (or Y = wage income) X = went through experimental program, or not Class size effect (Tennessee class size experiment) Y = test score (Stanford Achievement Test) X =being in a small class

6 The Potential Outcome Framework 6 Suppose we want to know the causal effect of a binary treatment X i on the outcome Y i For example let Y i be health and the treatment is a new medicine with X i = 1 takes new medicine X i = 0 does not take new medicine For each individual there exist two potential outcomes Y i (1) is the outcome of individual i if he takes the new medicine Y i (0) is the outcome of individual i if he does not take the new medicine The causal effect of the treatment on the outcome of individual i is Causal effect i = Y i (1) Y i (0)

7 The Potential Outcome Framework 7 The observed outcome Y i can be written in terms of the potential outcomes: Y i = Y i (1) X i + Y i (0) (1 X i ) If the individual received the treatment (X i = 1): Y i = Y i (1) 1 + Y i (0) 0 = Y i (1) If the individual did not receive the treatment (X i = 0): Y i = Y i (1) 0 + Y i (0) 1 = Y i (0) The identification problem: We cannot identify the causal effect for individual i because we observe either Y i (1) or Y i (0) but never both!

8 8 The Potential Outcome Framework & A Randomized Experiment Although we can never observe the causal effect for individual i, we might be able to estimate the average causal effect in a population. The average causal effect/ average treatment effect: E [Y i (1) Y i (0)] = E [Y i (1)] E [Y i (0)] Suppose we set up a ideal randomized experiment we take a random sample of the population we randomly give half of the sample the treatment, the other half does not get the treatment.

9 9 The Potential Outcome Framework & A Randomized Experiment The potential outcomes can differ between individuals Y i (1) Y j (1) and Y i (0) Y j (0) for i j However if the treatment X i is randomly assigned the distribution of potential outcomes will be the same in the treatment group (X i = 1) and in the control group (X i = 0) With random assignment the potential outcomes are independent of the treatment Y i (1), Y i (0) X i We thus have that E [Y i (1) X i = 1] = E [Y i (1) X i = 0] E [Y i (0) X i = 1] = E [Y i (0) X i = 0]

10 10 The Potential Outcome Framework & A Randomized Experiment In a randomized experiment individuals are randomly assigned to a treatment and control group, we therefore have that E [Y i (1)] = E [Y i (1) X i = 1] = E [Y i X i = 1] E [Y i (0)] = E [Y i (0) X i = 0] = E [Y i X i = 0] This implies that E [Y i (1) Y i (0)] = E [Y i (1)] E [Y i (0)] = E [Y i X i = 1] E [Y i X i = 0] We can thus estimate the average causal effect of the treatment by taking the difference in mean outcomes of the individuals in the treated group and control group

11 Example: project Star 11 A large-scale and influential randomized experiment: Project STAR (Student-Teacher Achievement Ratio) Kindergarten students and their teachers were randomly assigned to one of three groups beginning in the school year: small classes (13-17 students per teacher), regular-size classes (22-25 students), regular/aide classes (22-25 students) which also included a full-time teacher s aide. Over all 4 years about 11,600 students from 80 schools participated in the experiment Project STAR was funded by the Tennessee legislature, at a total cost of approximately \$12 million over four years.

12 Example: project Star 12 Kindergarten students were randomly assigned to 3 groups To simplify we combine the regular-size classes and the regular-size classes with an aide into 1 group This gives two groups: A treatment group (X i = 1): students assigned to a small class (13-17 students) A control group (X i = 0): students assigned to a regular class (22-25 students) We are interested in the causal effect of class size on student achievement. The outcome variable Y i is the Stanford Achievement Test score at the end of kindergarten.

13 Example: project Star 13 For each student i we have two potential outcomes: Y i (1) is the test score in case student i would be in a small class Y i (0) is the test score in case student i would be in a regular class The causal effect of class size on test score for pupil i is Y i (1) Y i (0) this is unobserved. Because students were randomly assigned to the treatment group (small class) and the control group (regular class) we can estimate the mean causal effect E [Y i (1) Y i (0)] by comparing mean test scores of the students in a small class (E [Y i X i = 1]) with the mean test scores of students in a regular class (E [Y i X i = 0])

14 Example: project Star 14 Mean test score students in regular class: E [Y i X i = 0] = Tuesday April 1 14:28: Page 1 Mean test score students in small class: E [Y i X i = 1] = (R) / / / / / Estimate of average causal effect: E [Y i X / i = 1] / E / / [Y i X i / = 0] / / = ttest testscore, by(small_class) Two-sample t test with equal variances Statistics/Data Analysis Group Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] combined diff diff = mean( 0) - mean( 1) t = Ho: diff = 0 degrees of freedom = 5784 Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0 Pr(T < t) = Pr( T > t ) = Pr(T > t) =

15 From Potential Outcomes to Regression 15 Consider subject i drawn at random from a population and let: X i = 1 if subject is treated, X i = 0 if not (binary treatment) Y i (0) =potential outcome for subject i if untreated Y i (1) =potential outcome for subject i if treated We saw on slide 7 that we can write the observed outcome as a function of the potential outcomes: Y i = Y i (1) X i + Y i (0) (1 X i ) rewrite i i i i add & subtract E [Y i (0)] = E [Y i (0)] + [Y i (1) Y i (0)] X i + [Y i (0) E [Y i (0)]]

16 From Potential Outcomes to Regression 16 Let, β 0 = E [Y i (0)] β 1i = [Y i (1) Y i (0)] is the causal effect for individual i u i = [Y i (0) E [Y i (0)]] Then we have Y i = E [Y i (0)] + [Y i (1) Y i (0)] X i + [Y i (0) E [Y i (0)]] }{{}}{{}}{{} β 0 β 1i If the causal effect is the same for all i, β 1i = β 1 for i = 1,..., n, we obtain the usual regression model Y i = β 0 + β 1 X i + u i u i

17 17 Potential Outcomes, Regression & a Randomized Experiment In an ideal randomized experiment we have that the potential outcomes are independent of the treatment Y i (1), Y i (0) X i We can thus estimate the average causal effect of the treatment by E [Y i X i = 1] E [Y i X i = 0] In a regression framework this implies that: receiving the treatment is unrelated to the error term: E [u i X i ] = 0 We can thus estimate the average causal effect of the treatment by using OLS to estimate Y i = β 0 + β 1 X i + u i Differences Estimator: β 1 = E [Yi X i = 1] E [Yi X i = 0]

18 Example: project Star 18 Tuesday April 1 15:58: Page 1 We can therefore also estimate the average causal effect of class (R) size / / / / / by estimating a simple regression model 1. regress testscore small_class / / / / / / / Statistics/Data Analysis Source SS df MS Number of obs = 5786 F( 1, 5784) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = testscore Coef. Std. Err. t P> t [95% Conf. Interval] small_class _cons β0 = E [Y i (0)] = β1 = E [Y i (1) Y i (0)] = is estimated average causal effect of being in a small class instead of a regular class

19 Randomization conditional on covariates 19 In some experiments the treatment is randomly assigned conditional on individual characteristics For example, let Y i be earnings and X i = 1 if individual is assigned to the treatment group that participates in a job training program X i = 0 if individual is assigned to the control group that does not participate in a job training program Suppose that the random assignment is conditional on the level of education where 60% of low educated individuals are randomly assigned to the job training program, 40% of high educated individuals are randomly assigned to the job training program

20 Randomization conditional on covariates 20 Education i X i Y i (0) Y i (1) causal effect Y i high high high high high low low low low low E [Y i X i = 1] E [Y i X i = 0] = = 14 6 = 8 However, if we estimate effect conditional on education: E [Y i X i = 1, high] E [Y i X i = 0, high] = = = 10 E [Y i X i = 1, low] E [Y i X i = 0, low] = = 10 0 = 10

21 Randomization conditional on covariates 21 In a regression framework: If we estimate Y i = β 0 + β 1 X i + u i, the conditional mean zero assumption (E [u i X i ] = 0) will be violated. The individuals in the control group are on average higher educated than the individuals in the treatment group High educated individuals generally have higher earnings. β1 will be a biased estimate of the average causal effect of the job training program due to omitted variable bias.

22 Randomization conditional on covariates 22 However, conditional on education assignment to the treatment group is random If we include education as control variable we can obtain an unbiased estimate of the average causal effect of the job training program We will however not obtain an unbiased estimate of the effect of education, because education is likely correlated with unobserved characteristics (ability, motivation)

23 Conditional Mean Independence (S&W appendix 7.1) 23 Suppose we have the following regression model Y i = β 0 + β 1 X i + β 2 W i + u i Y i is earnings X i equals 1 if individual participated in job training program W i equals 1 for high educated individuals 0 for low educated individuals Until now we have always defined the first OLS assumption to be E [u i X i, W i ] = 0 This means that both X i and W i are uncorrelated with the error term In the example W i is likely correlated with u i But conditional on education treatment assignment is random, so conditional on W i, X i is uncorrelated with u i

24 Conditional Mean Independence (S&W appendix 7.1) 24 Conditional Mean Independence : E [u i X i, W i ] = E [u i W i ] 0 Under Conditional Mean Independence, OLS will give an unbiased estimate of the causal effect of X i : Y i = β 0 + β 1 X i + β 2 W i + u i E [Y i X i = 1, W i ] = β 0 + β 1 + β 2 W i + E [u i X i = 1, W i ] E [Y i X i = 0, W i ] = β 0 + β 1 + β 2 W i + E [u i X i = 0, W i ] E [Y i X i = 1, W i ] E [Y i X i = 0, W i ] = β 1 + E [u i X i = 1, W i ] E [u i X i = 0, W i ] = β 1 + E [u i W i ] E [u i W i ] = β 1

25 Conditional Mean Independence (S&W appendix 7.1) 25 Conditional Mean Independence : E [u i X i, W i ] = E [u i W i ] 0 Under Conditional Mean Independence, OLS will give a biased estimate of the causal effect of W i Y i = β 0 + β 1 X i + β 2 W i + u i E [Y i X i, W i = 1] = β 0 + β 1 X i + β 2 + E [u i X i, W i = 1] E [Y i X i, W i = 0] = β 0 + β 1 X i + β 2 + E [u i X i, W i = 0] E [Y i X i, W i = 1] E [Y i X i, W i = 0] = β 2 + E [u i X i, W i = 1] E [u i X i, W i = 0] = β 2 + E [u i W i = 1] E [u i W i = 0] β 2 This is unproblematic as long as we are only interested in the causal effect of X i and not in the causal effect of W i

26 Conditional Mean Independence (S&W appendix 7.1) 26 Concept of Conditional Mean Independence also relevant in studies with observational data. Often we are interested in obtaining an unbiased & consistent estimate of 1 particular variable X i on an outcome Y i We generally include control variables W 1i,..., W ri to eliminate omitted variable bias This will give an unbiased & consistent estimate of the effect of X i if E [u i X i, W 1i,..., W ri ] = E [u i W 1i,..., W ri ] But often we don t obtain unbiased & consistent estimates of W 1i,..., W ri because E [u i W 1i,..., W ri ] 0

27 The differences estimator with additional regressors 27 One reason to include control variables is when assignment is random conditional on observed characteristics Another reason is to increase the precision of the estimate of the average treatment effect. If you observe pre-treatment characteristics that affect the outcome variable, you can include these to reduce the variance of the error term Y i = β 0 + β 1 X i + ε i Y i = β 0 + β 1 X i + δ 1 W 1i δ r W ri + u i Var (ε i ) > Var (u i ) This will reduce the standard error of the estimated effect of the treatment. But never include post-treatment characteristics, these are bad controls!

28 Example: project Star 28 (R) / / / / / / / / / / / / Statistics/Data Analysis 1. regress testscore small_class Source SS df MS Number of obs = 5786 F( 1, 5784) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = testscore Coef. Std. Err. t P> t [95% Conf. Interval] small_class _cons regress testscore small_class boy Source SS df MS Number of obs = 5786 F( 2, 5783) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = testscore Coef. Std. Err. t P> t [95% Conf. Interval] small_class boy _cons

29 Threats to internal validity in a randomized experiment 29 Analyzing data from an ideal randomized experiment will give an unbiased & consistent estimate of the causal effect of the treatment. In practice, setting up an ideal randomized experiment is not easy and often things do not go as planned This can lead to the following threats to internal validity Failure to randomize Failure to follow the treatment protocol Attrition Experimental effects/ Hawthorne effect Small samples

30 Failure to randomize 30 The treatment might not be assigned randomly but instead is based on characteristics or preferences of the subjects If this is due to the fact that the experimenter assigned the treatment randomly conditional on observed characteristics......we can estimate the causal effect by including these observed characteristics in the regression (conditional mean independence) If the treatment is randomly assigned conditional on unobserved characteristics or preferences......the estimated treatment effect will reflect both the effect of the treatment and the effect of these unobserved characteristics.

31 Failure to randomize 31 We can check whether the treatment was randomly assigned by comparing observed characteristics between the treatment and control group. Table shows mean characteristics of students assigned to small vs regular classes in project STAR Mean Mean Difference p-value small class regular class Gender (boy=1) Race (black=1) Eligible for free lunch No significant difference in the observed characteristics between those assigned to the treatment group (small class) and the control group (regular class)

32 Failure to follow the treatment protocol 32 Even if treatment assignment is random, treatment receipt might not be. Individuals assigned to the treatment group might not receive the treatment for example if individuals assigned to a job training program do not show up for the training sessions Individuals assigned to the control group might receive the treatment for example if individuals assigned to the control group manage to convince the instructor and attend the training sessions. If X i equals 1 if an individual received the treatment and 0 otherwise......regressing Y i on X i will give a biased estimate of the treatment effect. Treatment received is related to (unobserved) characteristics and preferences!

33 Failure to follow the treatment protocol 33 If we have data on the treatment actually received X i and on the initial random assignment Z i.....we can use the instrumental variable approach to estimate the treatment effect. Y i = β 0 + β 1 X i + u i X i = π 0 + π 1 Z i + v 1 We can use the initial random assignment as instrument for the treatment actually received! Instrument relevance: Cov(Z i, X i ) 0 as long as treatment assignment partially determines the treatment received, this condition holds. Instrument exogeneity: Cov(Z i, u i ) = 0 as long as treatment assignment is random, this condition holds.

34 Attrition 34 Attrition refers to subjects dropping out of the study after being randomly assigned to the treatment or control group Not problematic if attrition is unrelated to the treatment. If attrition is related to the treatment, the OLS estimator of the treatment effect will be biased For example if individuals that participated in the job training program moved out of town because they found a better job (due to the training) This is a reincarnation of sample selection bias from Ch. 9 (the sample is selected in a way related to the outcome variable).

35 Experimental effect/ Hawthorne effect 35 Hawthorne effect: Human subjects might change their behavior, merely because they are part of an experiment. For example, teachers assigned to small classes might put in extra effort They would like the researchers to find a positive effect of small class size. Teachers like to teach small classes.

36 Experimental effect/ Hawthorne effect 36 In some experiments, a double-blind protocol can mitigate the Hawthorne effect subjects and experimenters know that they are in an experiment......but neither knows which subjects are in the treatment group and which in the control group. In this case the treatment & control group experience the same experimental effects and differences in outcomes can be attributed to the treatment. Unfortunately double-blind experiments are often not feasible within the field of economics.

37 Small samples 37 Experiments with human subjects can be expensive. The sample size in experiments is therefore sometimes (too) small. Small samples do not produce biased estimates, but often produce imprecise estimates (large se s). In addition large-sample approximations might not be justified and confidence intervals and hypothesis test might not be valid.

38 Threats to external validity in a randomized experiment 38 Can we generalize the results based on the randomized experiment to other settings and populations? Nonrepresentative sample: The population studied and the population of interest might differ. Often experiments use subjects that signed up for participation in the experiment (volunteers) These volunteers are often more motivated. Even if these volunteers are randomly assigned to treatment and control group......the estimated average treatment effect might not be informative for a general population.

39 Threats to external validity in a randomized experiment 39 Nonrepresentative program or policy: Program or policy of interest might differ from the program studied. Experimental program is often small scale and tightly monitored. The quality of the actual program, when widely implemented, might therefore be lower than the experimental program. General equilibrium effects: Turning a small experimental program into a widespread, permanent program might change the economic environment. An experiment testing a small scale job training program might find positive effects on earnings. A large scale government funded job training program might not be beneficial if it crowds out employer funded training.

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