Lesson 19

Policy Evaluation and Self-Selection

Big question

Why is a treatment dummy not automatically causal?

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain policy evaluation and self-selection in plain language.
  • Use group-specific slope correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

A treatment dummy compares participants and nonparticipants. It becomes causal only under a credible design, such as random assignment or convincing controls.

Key terms

Group-specific slope
A slope that differs across groups because a dummy interacts with a continuous variable.
Chow test
A joint test for whether regression parameters differ across two groups.
Base group
The omitted category used as the comparison group for dummy coefficients.

Core formula

yi=β0+τtreatmenti+controlsi′γ+uiy_i = \beta_0 + \tau treatment_i + controls_i'\gamma + u_i

Use plain-language interpretation before algebra.

Example

MODULE7_PROGRAM_EVALUATION_SYNTHETIC is an original Ceteris Lab synthetic teaching dataset for policy evaluation and self-selection. It lets students practice program evaluation without presenting fabricated real-world empirical findings.

Interactive visual

ProgramEvaluationCausalityChecklist: use the controls to code a group, choose a base group, and write one correct interpretation.

Original Module 7 visual for Policy Evaluation and Self-Selection.

wage_sample.csv

y variable

wage

The dependent variable. It is the outcome students want to explain.

x variable

education

The explanatory variable. It is used to describe changes in wage.

Live Python

Policy Evaluation and Self-Selection Python example

Policy Evaluation and Self-Selection Python example

Stdout

Run Python to see results here.

Status / stderr

Ready to run Python in your browser.

Line-by-line guide

  1. Line 1Load a Python library needed for data work or regression.
  2. Line 2Load a Python library needed for data work or regression.
  3. Line 4Load the dataset into a pandas DataFrame.
  4. Line 5Create or update a Python object used in the analysis.
  5. Line 6Create or update a Python object used in the analysis.
  6. Line 7Run this Python instruction as part of the lesson workflow.
  7. Line 8Add an intercept column to the regression design matrix.
  8. Line 9Display a result so students can inspect the output.

Python walkthrough

  1. 1Load pandas and statsmodels so the workflow is reproducible.
  2. 2Read the installed Ceteris Lab synthetic CSV from the public data folder.
  3. 3Create or inspect dummy variables before estimating the model.
  4. 4Estimate OLS with an intercept and the selected regressors.
  5. 5Print coefficients or summaries, then interpret them as associations unless the design supports causality.

Live notebook

Run this lesson as a notebook

Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.

Open live notebookDownload .ipynbDownload .py

Related dataset

MODULE7_PROGRAM_EVALUATION_SYNTHETIC

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

  • Load and inspect MODULE7_PROGRAM_EVALUATION_SYNTHETIC.
  • Run the Python cells connected to Policy Evaluation and Self-Selection.
  • Interpret the output using dummy variables and qualitative information.

Common errors

  • File not found: check that module7_program_evaluation_synthetic.csv is installed or use the course data folder.
  • Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
  • Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/module-7/module7_program_evaluation_synthetic.csv")
df.head()

Interactive activity

ProgramEvaluationCausalityChecklist

Policy Evaluation and Self-Selection

ProgramEvaluationCausalityChecklist: choose the coding rule, base group, or probability interpretation before reading the coefficient.

MODULE7_PROGRAM_EVALUATION_SYNTHETIC

Inputs

Pick the next report ingredient

Try it yourself

Write one plain-English sentence explaining the main idea from this lesson.

Common mistakes

Check these before you move on.

Return to the lesson assumptions, units, diagnostics, and source evidence to replace this shortcut with a defensible interpretation.

Quick quiz

Which interpretation is most careful for Policy Evaluation and Self-Selection?

Quick quiz

What should students check before trusting the result in Policy Evaluation and Self-Selection?

Quick quiz

Why is MODULE7_STUDENT_COMPLETION_SYNTHETIC a reasonable practice dataset here?

Key takeaway

Policy Evaluation and Self-Selection helps students convert qualitative information into transparent, testable regression comparisons.

Download dataDownload resultsDownload interpretation