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Propensity Score Matching

Propensity Score Matching. TAF-CEGA Impact Evaluation Workshop Prashant Bharadwaj, UCSD, March 25 th , 2010. Program Evaluation Methods. RANDOMIZATION (EXPERIMENTS) QUASI-EXPERIMENTS Regression Discontinuity Difference-in-Differences Matching, Propensity Score. Matching Methods.

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Propensity Score Matching

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  1. Propensity Score Matching TAF-CEGA Impact Evaluation Workshop Prashant Bharadwaj, UCSD, March 25th, 2010

  2. Program Evaluation Methods • RANDOMIZATION (EXPERIMENTS) • QUASI-EXPERIMENTS • Regression Discontinuity • Difference-in-Differences • Matching, Propensity Score

  3. Matching Methods • Creating a counterfactual • To measure the effect of a program, we want to measure E[Y | D = 1, X] - E[Y | D = 0, X] but we only observe one of these outcomes for each individual.

  4. Basic Idea • Match each participant (treated) with one or more nonparticipants (untreated) with similar observed characteristics • Counterfactual = matched comparison group (i.e. nonparticipants with same characteristics as participants) • Job Training Example

  5. Let us match on some characteristics Education Sex Height Can figure out better match based on geography, parental education, smoking behavior

  6. Basic Idea Why did you take up job training? • This assumes that there is no selection bias based on unobserved characteristics • i.e. there is “selection on observables” and participation is independent of outcomes once we control for observable characteristics (X)

  7. Propensity Score • When the set of observed variables is large, we match participants with non participants using a summary measure: • the propensity score: the probability of participating in the program (being treated), as a function of the individual’s observed characteristics P(X) = Prob(D = 1|X) • D indicates participation in project • X is the set of observable characteristics

  8. Propensity Score • We maintain the assumption of selection on observables: • i.e., assume that participation is independent of outcomes conditional on Xi E (Y|X, D = 1) = E (Y|X, D = 0) if there had not been a program • This is false if there are unobserved outcomes affecting participation

  9. Evaluation Exercise Argentine Antipoverty Program

  10. Propensity Score Matching • Get representative and comparable data on participants and nonparticipants (ideally using the same survey & a similar time period)

  11. Propensity Score Matching • Get representative and comparable data on participants and nonparticipants (ideally using the same survey & a similar time period) • Estimate the probability of program participation as a function of observable characteristics (using a logit or other discrete choice model)

  12. Jalan and Ravallion (2003)

  13. Propensity Score Matching • Get representative and comparable data on participants and nonparticipants (ideally using the same survey & a similar time period) • Estimate the probability of program participation as a function of observable characteristics (using a logit or other discrete choice model) • Use predicted values from estimation to generate propensity score p(xi) for all treatment and comparison group members

  14. Propensity Score Matching • Match Participants: Find a sample of non-participants with similar p(xi) • Restrict samples to ensure common support

  15. Common Support Density Density of scores for non- participants Density of scores for participants Region of common support High probability of participating, given X 0 Low probability of participating, given X 1 Propensity score

  16. Propensity Score Matching • Match Participants: Find a sample of non-participants with similar p(xi) • Restrict samples to ensure common support • Determine a tolerance limit: • how different can matched control individuals or villages be? • Decide on a matching technique • Nearest neighbors, nonlinear matching, multiple matches

  17. Propensity Score Matching • Once matches are made, we can calculate impact by comparing the means of outcomes across participants and their matches • The difference in outcomes for each participant and its match is the estimate of the gain due to the program for that observation. • Calculate the mean of these individual gains to obtain the average overall gain.

  18. Possible Scenarios • Case 1: Baseline Data Exists • Arrive at baseline, we can match participants with nonparticipants using baseline characteristics. • Case 2: No Baseline Data. • Arrive afterwards, we can only match participants with nonparticipants using time-invariant characteristics.

  19. Extensions • Be cautious of ex-post matching • Matching on variables that change due to program participation (i.e. endogenous variables) • What are some invariable characteristics?

  20. Key Factors • Identification Assumption • Selection on Observables: After controlling for observables, treated and control groups are not systematically different • Data Requirements • Rich data on as many observable characteristics as possible • Large sample size (so that it is possible to find appropriate match)

  21. Additional Considerations • Advantages • Might be possible to do with existing survey data • Doesn’t require randomization/experiment/baseline data • Allows estimation of heterogeneous treatment effects because we have individual counterfactuals, instead of just having group averages.

  22. Additional Considerations • Disadvantages • Strong (if not heroic) identifying assumption: that there are no unobserved differences • but if individuals are otherwise identical, then why did some participate and others not? • Requires good quality data • Need to match on as many characteristics as possible • Requires sufficiently large sample size • Need a match for each participant in the treatment group

  23. Jalan & Ravallion (2003b) Does piped water reduce diarrhea for children in rural India?

  24. Data • Rural Household Survey • No baseline data • Detailed information on: • Health status of household members • Education levels of household members • Household income • Access to piped water • What would you use for D, Y, and X?

  25. Propensity Score Regression

  26. Propensity Score Regression

  27. Matching • Prior to matching, the estimated propensity scores for those with and without piped water were, respectively, • 0.5495 and 0.1933. • After matching there was negligible difference in the mean propensity scores of the two groups • 0.3743, for those with piped water • 0.3742, for the matched control group

  28. Results “Prevalence and duration of diarrhea among children under five in rural India are significantly lower on average for families with piped water than for observationally identical households without it.” “However, our results indicate that the health gains largely by-pass children in poor families, particularly when the mother is poorly educated.”

  29. Conclusion • Matching is a useful way to control for OBSERVABLE heterogeneity • Especially when randomization or RD approach is not possible • However, it requires relatively strong assumptions

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