11.1.1 Ideal Preceptor Table

Recall the ideal Preceptor Table. What rows and columns of data do you need such that, if you had them all, the calculation of the number of interest would be trivial? If you want to know the average height of an adult in India, then the ideal Preceptor Table would include a row for each adult in India and a column for their height.

One key aspect of this ideal Preceptor Table is whether or not we need more than one potential outcome in order to calculate our estimand. Mainly: do we need a causal model, one which estimates that attitude under both treatment and control? The Preceptor Table would require two columns for the outcome. In this case, we are trying to see the causal effect of mailed voting reminders on voting.

Are we are modeling (just) for prediction or are we (also) modeling for causation? Predictive models care nothing about causation. Causal models are often also concerned with prediction, if only as a means of measuring the quality of the model. Here, we are looking at causation.

So, what would our ideal table look like? Assuming we are running for governor in the United States, we would ideally have data for every citizen of voting age. This means we would have approximately 200 million rows.

Because there is no missing data in an ideal Preceptor Table, we would also know the outcomes under both treatment (receiving a reminder) and control (not receiving a reminder). Here is a sample row from our table:

In our ideal table, we have rows for all American citizens of voting age. This is a good start! However, we may want even more information in our ideal Preceptor Table. Perhaps a column for sex would be informative. A column for age? Political affiliation? In a perfect world, we would know all of these pieces of information. In a perfect world, we could measure the exact causal effect of voting reminders for different subsets of the US population.

We may also want to narrow our ideal Preceptor Table. If we are running for governor in Florida, we may only want to study citizens in Florida. If we are running as a Democrat, we may only want to study citizens who are registered Democrats.

However, the main point of this exercise is to see what we want to know compared with what we actually do know.

11.1.2 EDA of shaming

After loading the packages we need, let’s perform an EDA, starting off by running glimpse() on the shaming tibble from the primer.data package.

library(tidyverse)
library(primer.data)
library(rstanarm)
library(ggthemes)
library(ggdist)
library(gt)
library(janitor)
library(broom.mixed)
library(gtsummary)

glimpse(shaming)

## Rows: 344,084
## Columns: 15
## $ cluster       <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "…
## $ primary_06    <int> 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1,…
## $ treatment     <fct> Civic Duty, Civic Duty, Hawthorne, Hawthorne, Hawthorne,…
## $ sex           <chr> "Male", "Female", "Male", "Female", "Female", "Male", "F…
## $ age           <int> 65, 59, 55, 56, 24, 25, 47, 50, 38, 39, 65, 61, 57, 37, …
## $ primary_00    <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "N…
## $ general_00    <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "…
## $ primary_02    <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "…
## $ general_02    <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "…
## $ primary_04    <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "N…
## $ general_04    <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
## $ hh_size       <int> 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 1,…
## $ hh_primary_04 <dbl> 0.095, 0.095, 0.048, 0.048, 0.048, 0.048, 0.048, 0.048, …
## $ hh_general_04 <dbl> 0.86, 0.86, 0.86, 0.86, 0.86, 0.90, 0.90, 0.90, 0.90, 0.…
## $ neighbors     <int> 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, …

glimpse() gives us a look at the raw data contained within the shaming data set. At the very top of the output, we can see the number of rows and columns, or observations and variables respectively. We see that there are 344,084 observations, with each row corresponding to a unique respondent. This summary provides an idea of some of the variables we will be working with.

Variables of particular interest to us are sex, hh_size, and primary_06. The variable hh_size tells us the size of the respondent’s household, sex tells us the sex of the respondent, and primary_06 tells us whether or not the respondent voted in the 2006 Primary election. There are a few things to note while exploring this data set. You may – or may not – have noticed that the only response to the general_04 variable is “Yes.” In their published article, the authors note that “Only registered voters who voted in November 2004 were selected for our sample” (Gerber, Green, Larimer, 2008). After this, the authors found their history then sent out the mailings. Thus, non-registered voters are excluded from our data.

It is also important to identify the dependent variable and its meaning. In this shaming experiment, the dependent variable is primary_06, which is a variable coded either 0 or 1 for whether or not the respondent voted in the 2006 primary election. This is the dependent variable because the authors are trying to measure the effect that the treatments have on voting behavior in the 2006 general election.

We have not yet discussed the most important variable of them all: treatment. The treatment variable is a factor variable with 5 levels, including the control. Since we are curious as to how sending mailings affects voter turnout, the treatment variable will tell us about the impact each type of mailing can make. Let’s start off by taking a broad look at the different treatments.

shaming %>%
  count(treatment)

## # A tibble: 5 x 2
##   treatment       n
##   <fct>       <int>
## 1 Control    191243
## 2 Civic Duty  38218
## 3 Hawthorne   38204
## 4 Self        38218
## 5 Neighbors   38201

Four types of treatments were used in the experiment, with voters receiving one of the four types of mailing. All of the mailing treatments carried the message, “DO YOUR CIVIC DUTY - VOTE!”

The first treatment, Civic Duty, also read, “Remember your rights and responsibilities as a citizen. Remember to vote.” This message acted as a baseline for the other treatments, since it carried a message very similar to the one displayed on all the mailings.

In the second treatment, Hawthorne, households received a mailing which told the voters that they were being studied and their voting behavior would be examined through public records. This adds a small amount of social pressure to the households receiving this mailing.

In the third treatment, Self, the mailing includes the recent voting record of each member of the household, placing the word “Voted” next to their name if they did in fact vote in the 2004 election or a blank space next to the name if they did not. In this mailing, the households were also told, “we intend to mail an updated chart” with the voting record of the household members after the 2006 primary. By emphasizing the public nature of voting records, this type of mailing exerts more social pressure on voting than the Hawthorne treatment.

The fourth treatment, Neighbors, provides the household members’ voting records, as well as the voting records of those who live nearby. This mailing also told recipients, “we intend to mail an updated chart” of who voted in the 2006 election to the entire neighborhood.

11.1.3 Population

One of the most important components of Wisdom is the concept of the “population.” Recall the questions we asked earlier:

As we have discussed before, the population is not the set of people, or voters, for which we have data. This is the dataset. Nor is it the set of voters about whom we would like to have data. Those are the rows in the ideal Preceptor Table. The population is the larger — potentially much larger — set of individuals which include both the data we have and the data we want. Generally, the population will be much larger than either the data we have and the data we want.

In this case, we are viewing the data from the perspective of someone running for Governor this year that wants to increase voter turnout. We want to increase turnout now, not for people voting in 2006! We also may want to increase turnout in those citizens who are not registered to vote, a group that is excluded from our dataset. Is it reasonable to generate conclusions for this group? Most likely, no. However, we have limited data to work with and we have to determine how far we are willing to generalize to other groups.

It is a judgment call, a matter of Wisdom, as to whether or not we may assume that the data we have and the data we want to have (i.e., the ideal Preceptor Table) are drawn from the same population.

Even though the original question is about “voters” in general, and does not specifically refer to specific states in which we might be interested, we will assume that the data we have for random voters is, uh, representative enough of the population we are interested in. If we did not believe that, then we should stop right now. The major part of Wisdom is deciding what questions you can’t answer because of the data you just don’t have.

11.2.1 Actual Preceptor Table

Recall that in an actual Preceptor Table, we will have a bunch of missing data! We can not use simple arithmetic to calculate the causal effect of voting reminders on voting behavior. Instead, we will be required to estimate it. This is our estimand, a variable in the real world that we are trying to measure.An estimand is not the value you calculated, but is rather the unknown variable you want to estimate.

Let’s build a basic visualization for the actual Preceptor Table for this scenario:

Here, there are two possible outcomes: did vote or did not vote. What we really want to know is the Average Treatment Effect (ATE) of the treatment, the voting reminder. We want to estimate how much the voting reminder impacts the odds of someone voting.

Note that this is a simplified version of the actual Preceptor Table. In this dataset, we have a number of other columns that we know about each of our subjects: age, sex, past voting history. In an expanded actual Preceptor Table, these columns would be included.

Now, how can we fill in the question marks? Because of the Fundamental Problem of Causal Inference, we can never know the missing values. Because we can never know the missing values, we must make assumptions. “Assumption” just means that we need a “model,” and all models have parameters.

11.2.2 The Population Table

The Population Table shows the data that we actually have in our desired population. It shows rows from three sources: the ideal Preceptor Table, the data, and the population outside of the data (rows which exist but for which we have no data).

In our ideal Preceptor rows, we have information for our covariates of sex, year, and state. However, because those rows are not included in our data, we do not have any outcome results. Since our scenario pertains to an upcoming election in Texas, the state column will read Texas and the year column will read 2021.

The rows from our data have everything: the covariates and the outcomes. The covariates here will be Michigan for state and 2006 for year, since these are the pieces of information that are included in our data. Of course, we still do not have values for Treatment minus Control, since we cannot observe one subject under two conditions.

The rows from the population have no data. These are subjects which fall under our desired population, but for which we have no data. As such, all rows are missing.

11.2.3 Representativeness/validity

This is a good time to consider what it really means to accept that our data is representative of our population. With that in mind, let’s break down our real, current question:

• We are running for governor in Texas in the year 2021. In this year and in the United States, we consider sending out a voting reminder postcard to citizens of voting age. Will this reminder encourage voting, and by how much?

Now, let’s break down our data from the shaming dataset:

• The data was gathered in Michigan prior to the August 2006 primary election. The population for the experiment was 180,002 households in the state of Michigan. The data only included those who had voted in the 2004 general election. Therefore, it did not include non-voters. The reminders were mailed to households at random.

So, how similar are these groups? Let’s start with some differences. * The data is from 2006. Our question is asking for answers from 2021. This is not a small gap in time. A lot changes in a decade and a half! * The data excludes all non-voters in the last election. Our question, which seeks to increase voting turnout in all citizens, would want for non-voters to be included. So, can we make any claims about those citizens? Probably not. * The data only includes voters from Michigan. We want to make inferences about Texas, or perhaps the United States as a whole. Is it within reason to do that? * The voting reminders in the data were sent by mail. Is this the most practical option in 2021, where it may be easier to send an electronic notice? * The contents of voting reminders in 2006 are going to differ from voting reminders sent in 2021. Thus, our “treatments” are fundamentally different.

These are not small issues. These are big, in-our-face issues. If we say that data from voters in 2006 and data from voters in 2021 is exchangeable — meaning that they represent the same thing — we also have to consider what that means for other time frames. Can data from 2006 be used to predict for 2030, 2040, and beyond? What makes 2021 an acceptable option specifically?

It can be helpful in cases like these to make data exchangeable conditional on another assumption. For instance, if there was a trend of less voting in 2021 compared with 2006, we may need to account for that in order for our predictions to be helpful. Alternatively, if we were studying a state that has historically more voting participation than Michigan, we may want to make our predictions based on the assumption that “people, on average, vote more in State X.”

The purpose of this section is to make us think critically about the assumptions we are making and whether those assumptions can be reasonably made. Though we will continue using this dataset in the remainder of the chapter, it is clear that we must make our predictions with caution.

11.2.4 Functional form

11.3.1 Set-up

Now, we will create an object named object_1 that includes a 3-level factor classifying voters by level of civic engagement.

• Convert all primary and general election variables that are not already 1/0 binary to binary format.

• Create a new column named civ_engage that sums up each person’s voting behavior up to, but not including, the 2006 primary.

• Create a column named voter_class that classifies voters into 3 bins: “Always Vote” for those who voted at least 5 times, “Sometimes Vote” for those who voted between 3 or 4 times, and “Rarely Vote” for those who voted 2 or fewer times. This variable should be classified as a factor.

• Create a column called z_age which is the z-score for age.

object_1 <- shaming %>% 
  
  # Converting the Y/N columns to binaries with the function we made 
  # note that primary_06 is already binary and also that we don't 
  # need it to predict construct previous voter behavior status variable.
  
  mutate(p_00 = (primary_00 == "Yes"),
         p_02 = (primary_02 == "Yes"),
         p_04 = (primary_04 == "Yes"),
         g_00 = (general_00 == "Yes"),
         g_02 = (general_02 == "Yes"),
         g_04 = (general_04 == "Yes")) %>% 
  
  # A sum of the voting action records across the election cycle columns gives
  # us an idea (though not weighted for when across the elections) of the voters
  # general level of civic involvement.
  
  mutate(civ_engage = p_00 + p_02 + p_04 + 
                      g_00 + g_02 + g_04) %>% 
  
  # If you look closely at the data, you will note that g_04 is always Yes, so
  # the lowest possible value of civ_engage is 1. The reason for this is that
  # the sample was created by starting with a list of everyone who voted in the
  # 2004 general election. Note how that fact makes the interpretation of the
  # relevant population somewhat subtle.
  
  mutate(voter_class = case_when(civ_engage %in% c(5, 6) ~ "Always Vote",
                                 civ_engage %in% c(3, 4) ~ "Sometimes Vote",
                                 civ_engage %in% c(1, 2) ~ "Rarely Vote"),
         voter_class = factor(voter_class, levels = c("Rarely Vote", 
                                                      "Sometimes Vote", 
                                                      "Always Vote"))) %>% 
  
  # Centering and scaling the age variable. Note that it would be smart to have
  # some stopifnot() error checks at this point. For example, if civ_engage < 1
  # or > 6, then something has gone very wrong.
  
  mutate(z_age = as.numeric(scale(age))) %>% 
  select(primary_06, treatment, sex, civ_engage, voter_class, z_age)

Let’s inspect our object:

object_1 %>% 
  slice(1:3)

## # A tibble: 3 x 6
##   primary_06 treatment  sex    civ_engage voter_class    z_age
##        <int> <fct>      <chr>       <int> <fct>          <dbl>
## 1          0 Civic Duty Male            4 Sometimes Vote 1.05 
## 2          0 Civic Duty Female          4 Sometimes Vote 0.638
## 3          1 Hawthorne  Male            4 Sometimes Vote 0.361

Great! Now, we will create our first model: the relationship between primary_06, which represents whether a citizen voted or not, against sex and treatment.

11.3.2 primary_06 ~ treatment + sex

In this section, we will look at the relationship between primary voting and treatment + sex.

The math:

Without variable names:

\[ y_{i} = \beta_{0} + \beta_{1}x_{i, 1} + \beta_{2}x_{i,2} ... + \beta_{n}x_{i,n} + \epsilon_{i} \] With variable names:

\[ y_{i} = \beta_{0} + \beta_{1}civic\_duty_i + \beta_{2}hawthorne_i + \beta_{3}self_i + \beta_{4}neighbors_i + \beta_{5}male_i + \epsilon_{i} \]

There are two ways to formalize the model used in fit_1: with and without the variable names. The former is related to the concept of Justice as we acknowledge that the model is constructed via the linear sum of n parameters times the value for n variables, along with an error term. In other words, it is a linear model. The only other model we have learned this semester is a logistic model, but there are other kinds of models, each defined by the mathematics and the assumptions about the error term. The second type of formal notation, more associated with the virtue Courage, includes the actual variable names we are using. The trickiest part is the transformation of character/factor variables into indicator variables, meaning variables with 0/1 values. Because treatment has 5 levels, we need 4 indicator variables. The fifth level — which, by default, is the first variable alphabetically (for character variables) or the first level (for factor variables) — is incorporated in the intercept.

Let’s translate the model into code.

fit_1 <- stan_glm(data = object_1,
                  formula = primary_06 ~ treatment + sex,
                  refresh = 0,
                  seed = 987)

print(fit_1, digits = 3)

## stan_glm
##  family:       gaussian [identity]
##  formula:      primary_06 ~ treatment + sex
##  observations: 344084
##  predictors:   6
## ------
##                     Median MAD_SD
## (Intercept)         0.291  0.001 
## treatmentCivic Duty 0.018  0.003 
## treatmentHawthorne  0.026  0.003 
## treatmentSelf       0.048  0.002 
## treatmentNeighbors  0.081  0.003 
## sexMale             0.012  0.002 
## 
## Auxiliary parameter(s):
##       Median MAD_SD
## sigma 0.464  0.001 
## 
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanreg

We will now create a table that nicely formats the results of fit_1 using the tbl_regression() function from the gtsummary package. It will also display the associated 95% confidence interval for each coefficient.

tbl_regression(fit_1, 
               intercept = TRUE, 
               estimate_fun = function(x) style_sigfig(x, digits = 3)) %>%
  
  # Using Beta as the name of the parameter column is weird.
  
  as_gt() %>%
  tab_header(title = md("**Likelihood of Voting in the Next Election**"),
             subtitle = "How Treatment Assignment and Age Predict Likelihood of Voting") %>%
  tab_source_note(md("Source: Gerber, Green, and Larimer (2008)")) %>% 
  cols_label(estimate = md("**Parameter**"))

Interpretation: * The intercept of this model is the expected value of the probability of someone voting in the 2006 primary given that they are part of the control group and are female. In this case, we estimate that women in the control group will vote ~29.1% of the time. * The coefficient for sexMale indicates the difference in likelihood of voting between a male and female. In other words, when comparing men and women, the 0.01 implies that men are ~1.2% more likely to vote than women. Note that, because this is a linear model with no interactions between sex and other variables, this difference applies to any male, regardless of the treatment he received. Because sex can not be manipulated (by assumption), we should not use a causal interpretation of the coefficient. * The coefficients of the treatments, on the other hand, do have a causal interpretation. For a single individual, of either sex, being sent the Self postcard increases your probability of voting by 4.8%. It appears that the Neighbors treatment is the most effective at ~8.1% and Civic Duty is the least effective at ~1.8%.

11.3.3 primary_06 ~ z_age + sex + treatment + voter_class + voter_class*treatment

It is time to look at interactions! Create another model named fit_2 that estimates primary_06 as a function of z_age, sex, treatment, voter_class, and the interaction between treatment and voter classification.

The math: \[y_{i} = \beta_{0} + \beta_{1}z\_age + \beta_{2}male_i + \beta_{3}civic\_duty_i + \\ \beta_{4}hawthorne_i + \beta_{5}self_i + \beta_{6}neighbors_i + \\ \beta_{7}Sometimes\ vote_i + \beta_{8}Always\ vote_i + \\ \beta_{9}civic\_duty_i Sometimes\ vote_i + \beta_{10}hawthorne_i Sometimes\ vote_i + \\ \beta_{11}self_i Sometimes\ vote_i + \beta_{11}neighbors_i Sometimes\ vote_i + \\ \beta_{12}civic\_duty_i Always\ vote_i + \beta_{13}hawthorne_i Always\ vote_i + \\ \beta_{14}self_i Always\ vote_i + \beta_{15}neighbors_i Always\ vote_i + \epsilon_{i}\] Translate into code:

fit_2 <- stan_glm(data = object_1,
                  formula = primary_06 ~ z_age + sex + treatment + voter_class + 
                            treatment*voter_class,
                  family = gaussian,
                  refresh = 0,
                  seed = 789)

print(fit_2, digits = 3)

## stan_glm
##  family:       gaussian [identity]
##  formula:      primary_06 ~ z_age + sex + treatment + voter_class + treatment * 
##     voter_class
##  observations: 344084
##  predictors:   17
## ------
##                                               Median MAD_SD
## (Intercept)                                    0.153  0.003
## z_age                                          0.035  0.001
## sexMale                                        0.008  0.002
## treatmentCivic Duty                            0.010  0.007
## treatmentHawthorne                             0.008  0.007
## treatmentSelf                                  0.024  0.007
## treatmentNeighbors                             0.044  0.007
## voter_classSometimes Vote                      0.114  0.003
## voter_classAlways Vote                         0.294  0.004
## treatmentCivic Duty:voter_classSometimes Vote  0.014  0.007
## treatmentHawthorne:voter_classSometimes Vote   0.019  0.007
## treatmentSelf:voter_classSometimes Vote        0.030  0.008
## treatmentNeighbors:voter_classSometimes Vote   0.042  0.008
## treatmentCivic Duty:voter_classAlways Vote    -0.001  0.009
## treatmentHawthorne:voter_classAlways Vote      0.025  0.009
## treatmentSelf:voter_classAlways Vote           0.025  0.009
## treatmentNeighbors:voter_classAlways Vote      0.046  0.009
## 
## Auxiliary parameter(s):
##       Median MAD_SD
## sigma 0.451  0.001 
## 
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanreg

As we did with our first model, create a regression table to observe our findings:

tbl_regression(fit_2, 
               intercept = TRUE, 
               estimate_fun = function(x) style_sigfig(x, digits = 3)) %>%
  as_gt() %>%
  tab_header(title = md("**Likelihood of Voting in the Next Election**"),
             subtitle = "How Treatment Assignment and Other Variables Predict Likelihood of Voting") %>%
  tab_source_note(md("Source: Gerber, Green, and Larimer (2008)")) %>% 
  cols_label(estimate = md("**Parameter**"))

Now that we have a summarized visual for our data, let’s interpret the findings: * The intercept of fit_2 is the expected probability of voting in the upcoming election for a woman of average age (~ 50 years old in this data), who is assigned to the Control group, and is a Rarely Voter. The estimate is 15.3%. * The coefficient of z_age, 0, implies a change of ~3.5% in likelihood of voting for each increment of one standard deviation (~ 14.45 years). For example: when comparing someone 50 years old with someone 65, the latter is about 3.5% more likely to vote. * Exposure to the Neighbors treatment shows a ~4.4% increase in voting likelihood for someone in the Rarely Vote category. Because of random assignment of treatment, we can interpret that coefficient as an estimate of the average treatment effect. * If someone were from a different voter classification, the calculation is more complex because we need to account for the interaction term. For example, for individuals who Sometimes Vote, the treatment effect of Neighbors is 0.1%. For Always Vote Neighbors, it is 0.1%.

11.4.1 The Three Levels of Knowledge

There exist three primary levels of knowledge possible knowledge in our scenario: the Truth (the ideal Preceptor Table), the DGM Posterior, and Our Posterior.