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Causal inference

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Frame of Image mative strategies
o Before and after comparisons o Comparisons of participants and nonpar ticipants o Measuring only statistical correlation (or association)
Before and after comparisons
o Compare outcome of interest pre-intervention (t=0) to outcome post-intervention (t=1)
o Examples
o School decentralization: test scores o Conditional cash transfers: school attendance o Health insurance: health expenditure o Higher health worker salaries: absenteeism
Before and after comparisons
o Yields impact of intervention + whatever else happened between t=0 and t=1
o Concurrent trends o Other interventions, labor market shocks, aggregate health shocks
o Health insurance and simultaneous heavy rains
o Health insurance  increase utilization, decrease expenditure per utilization o Malaria epidemic  increase utilization and expenditure o Crop loss  decrease income and utilization o Impossible to disentangle changes between t=0 and t=1
o Health insurance (- or +) o Malaria (+) o Cross loss (-)
o Could underestimate or overestimate true impact of intervention
o Not just magnitude but also sign of the effect
Compare participants and non-participants
o Comparing units with intervention to units not part of intervention
o Gives intervention effect + whatever is different between participants and non-participants
o Selection bias o Programs usually targeted to certain areas or people o Individuals voluntarily apply or join a program
o CCTs
o Applicants more motivated than non-applicants  hi


Full Text
Title Causal inference
Similar Titles
Material Type Proceedings
Author(English)

Beath, Andrew; Trias, Julieta

Publisher

[Washington, DC]:The World Bank

Date 2012
Event

The Regional Impact Evaluation Workshop

Series Title; No Technical Track Session
Pages 22
Language English
File Type Documents
Original Format pdf
Subject Official Aid < Others
Holding KDI; KDI School

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