Downloaded 27 times
![PSM: Key Assumptions
Key assumption: participation is independent
of outcomes conditional on Xi
This is false if there are unobserved
outcomes affecting participation
Enables matching not just at the mean but
balances the distribution of observed
characteristics across treatment and control
[ ] [ ]0,|1,| 00 === dXyEdXyE](https://image.slidesharecdn.com/matchingmethods-170515091146/75/Matching-methods-4-2048.jpg)
Uploaded byFAIZY MANSOURY
Matching methods
Propensity score matching (PSM) is a quasi-experimental technique used to estimate causal treatment effects from observational data. It involves matching treated observations to untreated observations based on propensity scores, which represent the probability of receiving treatment given observed covariates. Key assumptions are that treatment assignment is independent of outcomes conditional on covariates, and there is sufficient overlap in covariate distributions between treated and untreated groups. PSM was used to estimate the impact of piped water access on child health in rural India by matching households based on village characteristics, assets, and education levels, though some important behavioral factors were unobserved.
In this document
Powered by AI
Introduction to matching methods and selection of comparison groups based on observed characteristics.
Explanation of PSM, its assumptions, and how it matches treated and untreated observations based on participation probability.
Concept of propensity score density and steps involved in calculating and using propensity scores for matching.
Process for matching pairs and comparing means; contrasts PSM with randomization, emphasizing data requirements.
Insights on the application of matching methods, discussing challenges and the importance of quality data.
Study examining the impact of piped water on children's health in India, emphasizing observational challenges.
Application of PSM using village and household level variables to estimate scores and analyze behavioral factors.
Discussion of potential unobserved behavioral factors that may affect health outcomes related to piped water.
Results indicating health impacts of piped water access, including unexpected outcomes for certain households.
Overview of different research design strategies, highlighting pros and cons to guide methodology selections.
More Related Content
Matching methods
- 1.
- 2. Matching: Overview Theideal comparison group is selected such that matches the treatment group using either a comprehensive baseline survey or time invariant characteristics The matches are selected on the basis of similarities in observed characteristics This assumes no selection bias based on unobserved characteristics Take the ITN Example from Yesterday: Households who were more concerned about malaria also took other preventative actions All such differences must be in the data in order for the match to produce a valid estimate of project impacts
- 3. Propensity-Score Matching (PSM) Propensityscore matching: match treated and untreated observations on the estimated probability of being treated (propensity score). Most commonly used. Match on the basis of the propensity score P(X) = Pr (d=1|X) D indicates participation in project Instead of attempting to create a match for each participant with exactly the same value of X, we can instead match on the probability of participation.
- 4. PSM: Key Assumptions Key assumption: participation is independent of outcomes conditional on Xi This is false if there are unobserved outcomes affecting participation Enables matching not just at the mean but balances the distribution of observed characteristics across treatment and control [ ] [ ]0,|1,| 00 === dXyEdXyE
- 5. Density 0 1Propensity score Regionof common support Density of scores for participants High probability of participating given X Density of scores for non- participants
- 6. Steps in ScoreMatching 1. Need representative and comparable data for both treatment and comparison groups 2. Use a logit (or other discrete choice model) to estimate program participations as a function of observable characteristics 3. Use predicted values from logit to generate propensity score p(xi) for all treatment and comparison group members
- 7. Calculating Impact usingPSM 4. Match Pairs: Restrict sample to common support (as in Figure) Need to determine a tolerance limit: how different can control individuals or villages be and still be a match? Nearest neighbors, nonlinear matching, multiple matches 5. Once matches are made, we can calculate impact by comparing the means of outcomes across participants and their matched pairs
- 8. PSM vs Randomization Randomization does not require the untestable assumption of independence conditional on observables PSM requires large samples and good data: 1. Ideally, the same data source is used for participants and non-participants 2. Participants and non-participants have access to similar institutions and markets, and 3. The data include X variables capable of identifying program participation and outcomes.
- 9. Lessons on MatchingMethods Typically used when neither randomization, RD or other quasi experimental options are not possible Case 1: no baseline. Can do ex-post matching Dangers of ex-post matching: Matching on variables that change due to participation (i.e., endogenous) What are some variables that won’t change? Matching helps control only for OBSERVABLE differences, not unobservable differences
- 10. More Lessons onMatching Methods Matching becomes much better in combination with other techniques, such as: Exploiting baseline data for matching and using difference-in-difference strategy If an assignment rule exists for project, can match on this rule Need good quality data Common support can be a problem if two groups are very different
- 11. Case Study: PipedWater in India Jalan and Ravaillion (2003): Impact of piped water for children’s health in rural India Research questions of interest include: 1. Is a child less vulnerable to diarrhoeal disease if he/she lives in a HH with access to piped water? 2. Do children in poor, or poorly educated, HH have smaller health gains from piped water? 3. Does income matter independently of parental education?
- 12. Piped Water: theIE Design Classic problem for infrastructure programs: randomization is generally not an option (although randomization in timing may be possible in other contexts) The challenge: observable and unobservable differences across households with piped water and those without What are differences for such households in Nigeria? Jalan and Ravallion use cross-sectional data 1993-1994 nationally representative survey on 33,000 rural HH from 1765 villages
- 13. PSM in Practice To estimate the propensity score, authors used: Village level characteristics Including: Village size, amount of irrigated land, schools, infrastructure (bus stop, railway station) Household variables Including: Ethnicity / caste / religion, asset ownership (bicycle, radio, thresher), educational background of HH members Are there variables which can not be included? Only using cross-section, so no variables influenced by project
- 14. Piped Water: BehavioralConsiderations IE is designed to estimate not only impact of piped water but to look at how benefits vary across group There is therefore a behavioral component: poor households may be less able to benefit from piped water b/c they do not properly store water With this in mind, Are there any key variables missing?
- 15. Potential Unobserved Factors The behavioral factors – importance put on sanitation and behavioral inputs – are also likely correlated with whether a HH has piped water However, there are no behavioral variables in data: water storage, soap usage, latrines These are unobserved factors NOT included in propensity score
- 17. Piped Water: Impacts Disease prevalence among those with piped water would be 21% higher without it Gains from piped water exploited more by wealthier households and households with more educated mothers Even find counterintuitive result for low income, illiterate HH: piped water is associated with higher diarrhea prevalence
- 18. Design When touse Advantages Disadvantages Randomization Whenever feasible When there is variation at the individual or community level Gold standard Most powerful Not always feasible Not always ethical Randomized Encouragement Design When an intervention is universally implemented Provides exogenous variation for a subset of beneficiaries Only looks at sub- group of sample Power of encouragement design only known ex post Regression Discontinuity If an intervention has a clear, sharp assignment rule Project beneficiaries often must qualify through established criteria Only look at sub- group of sample Assignment rule in practice often not implemented strictly Difference-in- Differences If two groups are growing at similar rates Baseline and follow- up data are available Eliminates fixed differences not related to treatment Can be biased if trends change Ideally have 2 pre- intervention periods of data Matching When other methods are not possible Overcomes observed differences between treatment and comparison Assumes no unobserved differences (often implausible)