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Experimental Methods : Technical Track

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Frame of Image Expectation Function
E[Yi | X i ]
Expected Value of Y, conditional on X. This is the mean value Y for the whole population, for every value of X.
For example: Y = wage; X = years of completed schooling
E[Y | X i = 4] = 6.1
Source: Angrist and Pishke (2009)
Recap
• Conditional Expectation Function • Linear Regression
Linear Regression
Yi = b 0 + b1 X i + e i
Best linear approximation of the relationship between variables
Y
= independent variable
X
= dependent variable
b 0 = intercept ei
= error
= slope (linear relationship between X and Y)
b1
Linear Regression
Y
X
Multivariate Linear Regression
Yi = b 0 + b1 X 1i + b 2 X 2i + e i
Linear relationship between X 1 and Y , after controlling for X 1
X 1i
CAUSAL INFERENCE
Our Objective
Estimate the causal effect (impact) of intervention (D) on outcome (Y).
r = E [Y1i - Y0i ]
D Yi Y1i Y0i
= Program or Treatment = Indicator, Measure of Success = Outcome with the program = Outcome without the program
Same Person with Two Potential Outcomes
ì Y1i = b + r + e i if D = 1 ü Yi = í ý if D = 0 þ îY0i = b + e i
Challenge – No counterfactual
r = E[Y1i - Y0i ]
We do not observe what would have happened to the same pupil if they did not receive any cash transfer (the counterfactual )
What We Observe
E[Yi | D = 1] - E[Yi | D = 0]
Difference between those in the treatment and those in the comparison group.
CLONE
PERFECT EXPERIMENT
Example: CCT Progresa
ü National anti-poverty program in Mexico ü Cash Transfers conditional on school and health care


Full Text
Title Experimental Methods
Similar Titles
Sub Title

Technical Track

Material Type Proceedings
Author(English)

Jacobus Cilliers

Date 2014-05
Event

East Asia Regional Impact Evaluation Wokshop

Pages 95
Language English
File Type Documents
Original Format pdf
Subject Territorial Development
Holding KDI School of Public Policy and Management