Lesson 15

Over-Control and Bad Controls

Big question

When can adding more controls make interpretation worse?

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain over-control and bad controls in plain language.
  • Use smearing factor correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

A bad control can block part of the pathway you want to study or introduce bias. More variables are not automatically better.

Key terms

Smearing factor
A retransformation adjustment for predicting y from a log(y) regression.
Elasticity
The approximate percent change in y associated with a one percent change in x.
Centering
Subtracting a reference value, often the mean, before creating powers or interactions.

Core formula

choosecontrolsfortheestimand,notformaximumR2choose controls for the estimand, not for maximum R^2

Use plain-language interpretation before algebra.

Example

M6_POLICY_CONTROLS is a synthetic teaching dataset for over-control and bad controls. It is designed to practice control selection without presenting fabricated real-world empirical findings.

Interactive visual

Use a causal story card to classify candidate controls as helpful, bad, or unclear.

Original Module 6 visual for Over-Control and Bad Controls.

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

Over-Control and Bad Controls Python example

Over-Control and Bad Controls 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 3Create or update a Python object used in the analysis.
  3. Line 4Run this Python instruction as part of the lesson workflow.
  4. Line 5Run this Python instruction as part of the lesson workflow.
  5. Line 6Run this Python instruction as part of the lesson workflow.
  6. Line 7Run this Python instruction as part of the lesson workflow.
  7. Line 8Display a result so students can inspect the output.

Python walkthrough

  1. 1Load the synthetic teaching dataset from the Module 6 public data folder.
  2. 2Create transformed variables only after checking their meaning and valid support.
  3. 3Fit a regression that matches the lesson's interpretation target.
  4. 4Print coefficient or prediction summaries that students can connect to the formula.
  5. 5Use comments and output labels so no empirical result is presented without context.

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

M6_POLICY_CONTROLS

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

A regression or inference table with coefficients, uncertainty, and short interpretation notes.

Learning goals

  • Load and inspect M6_POLICY_CONTROLS.
  • Run the Python cells connected to Over-Control and Bad Controls.
  • Interpret the output using bad controls and precision controls.

Common errors

  • File not found: check that M6_POLICY_CONTROLS.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-6/M6_POLICY_CONTROLS.csv")
df.head()

Interactive activity

BadControlDecisionTree

Classify controls

Decide whether a variable is a safe control, bad control, or unclear.

M6_POLICY_CONTROLS

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.

A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.

Quick quiz

Which interpretation is most careful for Over-Control and Bad Controls?

Quick quiz

What is the main mistake to avoid in Over-Control and Bad Controls?

Quick quiz

Why is M6_GPA_INTERACTIONS a reasonable practice dataset here?

Key takeaway

Over-Control and Bad Controls helps students make multiple regression more flexible while keeping interpretation precise and honest.

Download dataDownload resultsDownload interpretation