Skip to main content

Causal Inference Course

Discussion 2. Analyzing an Experiment in R

STSCI/INFO/ILRST 3900: Causal Inference

September 3, 2025

Announcements

  • HW 1 due Tuesday (September 9) by 5pm
    • Submit a PDF from RMarkdown via Canvas
  • Office Hours throughout the week (see Syllabus or website)
    • Filippo: Thursday 4-5pm in 321A CIS Building
    • Shira: Monday 5-6 pm in 329A CIS Building
    • Sam: Tuesday 4-5pm, CIS Building

Get out and Vote Experiment

  • Why do people vote?
  • One long-standing theory: People vote due to social norms (civic duty)
  • Empirical evidence for this theory was extremely thin
  • Research Question: to what extent do social norms cause voter turnout?
  • Article: “Social Pressure and Voter Turnout: Evidence from a Large-scale Field Experiment.”in American Political Science Review
  • Authors: Alan S. Gerber, Donald P. Green, and Christopher W. Larimer
  • We’ll be analyzing their experiment today!

Experimental Design

  • Approximately 80k Michigan households were randomly assigned 1 of 4 mailings encouraging them to vote
    1. Simply reminded them that voting is a civic duty
    2. Told that researchers would be studying their turnout based on public records
    3. Received record of voting turnout within their household
    4. Received record of voting turnout within their household and their neighbors’ households.
  • Third and fourth treatment arms were told that their turnout would be revealed as well

Goal for Today

Replicate Something Similar to Tables 1 and 2 from the article

Resources for Markdown

Step 1: Download the .Rmd file here

  • Start by running the code in Section “Necessary packages”
  • If you get an error, you may need to install the package

Step 2: Import and Clean the Data 

gotv <- read_dta("https://causal3900.github.io/assets/data/social_pressure.dta")
  • Quick peek at the dataset using the function glimpse
  • Notice that we have information about year of birth yob but not explicitly age
  • Notice that the treatments are labeled with the numbers 0 through 4
glimpse(gotv)
## Rows: 344,084
## Columns: 16
## $ sex           <dbl+lbl> 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,…
## $ yob           <dbl> 1941, 1947, 1951, 1950, 1982, 1981, …
## $ g2000         <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,…
## $ g2002         <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,…
## $ g2004         <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ p2000         <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ p2002         <dbl+lbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,…
## $ p2004         <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
## $ treatment     <dbl+lbl> 2, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0,…
## $ cluster       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ voted         <dbl+lbl> 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,…
## $ hh_id         <dbl> 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 6, …
## $ hh_size       <dbl> 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 1, 2, …
## $ numberofnames <dbl> 21, 21, 21, 21, 21, 21, 21, 21, 21, …
## $ p2004_mean    <dbl> 0.09523810, 0.09523810, 0.04761905, …
## $ g2004_mean    <dbl> 0.8571429, 0.8571429, 0.8571429, 0.8…
  • Calculate the ages of everyone in our dataset in the year 2006
  • Use mutatet() to construct an age variable
  • You can do arithmetic operations with information from the dataset
  • For example: mutate(col_3 = col_1 + col_2) 
gotv <- gotv |>
  mutate(age = )
  • Replace the numeric labels of treatment (0-4) with word labels:
    • 0: “Control”
    • 1: “Hawthorne” (this is the ‘researchers viewing records via public data’ treatment arm)
    • 2: “Civic Duty” (this is the ‘voting is your civic duty’ treatment arm)
    • 3: “Neighbors” (this is the ‘voting turnout revealed to neighbors’ treatment arm)
    • 4: “Self” (this is the ‘voting turnout revealed to household’ treatment arm)
  • For this, you will want to use the function case_when which is described here
  • The general syntax is case_when(condition ~ output-value)
  • A condition would be treatement == 0 and an output value would be "Control" 
gotv <- gotv |>
  mutate(treatment = case_when())
  • When you run glimpse(gotv), you should see something like this

Step 3: Table 1

  • Is the data balanced on covariates?
  • We want to check that the treatment groups are balanced on covariates
  • For each treatment arm/group, calculate the mean for each of the designated covariates:
    • Household size, Nov 2002, Nov 2000, Aug 2004, Aug 2002, Aug 2000, Female, Age (in years)
  • Use group_by() to calculate separate means for each treatment arm
  • Use summarise() to computes the mean of each covariate in covariates 
covariates <- c("sex", "age", "g2000", "g2002", "p2000", "p2002", "p2004", "hh_size")

gotv_balance <- gotv |>
  group_by(...) |>
  summarise(...)

print(gotv_balance)
  • Your table should look like this (covariates similar across the groups)

Step 4: Table 2

  • What are the results of the experiment?
  • For each treatment group, calculate the percent that voted and the total number of individuals in that group
  • Use group_by() to calculate separate means for each treatment arm
  • Use summarise() to to do the following:
    • Create a column Percentage_Voting- the percent that voted of each group
    • Create a column num_of_individuals- the total number of people in that group
gotv_results <- gotv |>
  group_by(...) |>
  summarise(...)

print(gotv_results)
  • Your table should look like this