---
title: "Using WeightIt to Estimate Balancing Weights"
author: "Noah Greifer"
date: "`r Sys.Date()`"
output: 
    html_vignette:
        df_print: kable
vignette: >
  %\VignetteIndexEntry{Using WeightIt to Estimate Balancing Weights}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown_notangle}
bibliography: references.bib
link-citations: true
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval=TRUE)
options(width = 200, digits= 4)

me_ok <- requireNamespace("marginaleffects", quietly = TRUE) && !isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))
set.seed(1000)
```

## Introduction

`WeightIt` contains several functions for estimating and assessing balancing weights for observational studies. These weights can be used to estimate the causal parameters of marginal structural models. I will not go into the basics of causal inference methods here. For good introductory articles, see @austinIntroductionPropensityScore2011, @austinMovingBestPractice2015, @robinsMarginalStructuralModels2000, or @thoemmesPrimerInverseProbability2016.

Typically, the analysis of an observation study might proceed as follows: identify the covariates for which balance is required; assess the quality of the data available, including missingness and measurement error; estimate weights that balance the covariates adequately; and estimate a treatment effect and corresponding standard error or confidence interval. This guide will go through all these steps for two observational studies: estimating the causal effect of a point treatment on an outcome, and estimating the causal parameters of a marginal structural model with multiple treatment periods. This is not meant to be a definitive guide, but rather an introduction to the relevant issues.

## Balancing Weights for a Point Treatment

First we will use the Lalonde dataset to estimate the effect of a point treatment. We'll use the version of the data set that resides within the `cobalt` package, which we will use later on as well. Here, we are interested in the average treatment effect on the treated (ATT).

```{r}
library("cobalt")
data("lalonde", package = "cobalt")
head(lalonde)
```

We have our outcome (`re78`), our treatment (`treat`), and the covariates for which balance is desired (`age`, `educ`, `race`, `married`, `nodegree`, `re74`, and `re75`). Using `cobalt`, we can examine the initial imbalance on the covariates:

```{r}
bal.tab(treat ~ age + educ + race + married + nodegree + re74 + re75,
        data = lalonde, estimand = "ATT", thresholds = c(m = .05))
```

Based on this output, we can see that all variables are imbalanced in the sense that the standardized mean differences (for continuous variables) and differences in proportion (for binary variables) are greater than .05 for all variables. In particular, `re74` and `re75` are quite imbalanced, which is troubling given that they are likely strong predictors of the outcome. We will estimate weights using `weightit()` to try to attain balance on these covariates.

First, we'll start simple, and use inverse probability weights from propensity scores generated through logistic regression. We need to supply `weightit()` with the formula for the model, the data set, the estimand (ATT), and the method of estimation (`"glm"`) for generalized linear model propensity score weights).

```{r}
library("WeightIt")
W.out <- weightit(treat ~ age + educ + race + married + nodegree + re74 + re75,
                  data = lalonde, estimand = "ATT", method = "glm")
W.out #print the output
```

Printing the output of `weightit()` displays a summary of how the weights were estimated. Let's examine the quality of the weights using `summary()`. Weights with low variability are desirable because they improve the precision of the estimator. This variability is presented in several ways: by the ratio of the largest weight to the smallest in each group, the coefficient of variation (standard deviation divided by the mean) of the weights in each group, and the effective sample size computed from the weights. We want a small ratio, a smaller coefficient of variation, and a large effective sample size (ESS). What constitutes these values is mostly relative, though, and must be balanced with other constraints, including covariate balance. These metrics are best used when comparing weighting methods, but the ESS can give a sense of how much information remains in the weighted sample on a familiar scale.

```{r}
summary(W.out)
```

These weights have quite high variability, and yield an ESS of close to 100 in the control group. Let's see if these weights managed to yield balance on our covariates.

```{r}
bal.tab(W.out, stats = c("m", "v"), thresholds = c(m = .05))
```

For nearly all the covariates, these weights yielded very good balance. Only `age` remained imbalanced, with a standardized mean difference greater than .05 and a variance ratio greater than 2. Let's see if we can do better. We'll choose a different method: entropy balancing [@hainmuellerEntropyBalancingCausal2012], which guarantees perfect balance on specified moments of the covariates while minimizing the entropy (a measure of dispersion) of the weights.

```{r}
W.out <- weightit(treat ~ age + educ + race + married + nodegree + re74 + re75,
                  data = lalonde, estimand = "ATT", method = "ebal")
summary(W.out)
```

The variability of the weights has not changed much, but let's see if there are any gains in terms of balance:

```{r}
bal.tab(W.out, stats = c("m", "v"), thresholds = c(m = .05))
```

Indeed, we have achieved perfect balance on the means of the covariates. However, the variance ratio of `age` is still quite high. We could continue to try to adjust for this imbalance, but if there is reason to believe it is unlikely to affect the outcome, it may be best to leave it as is. (You can try adding `I(age^2)` to the formula and see what changes this causes.)

Now that we have our weights stored in `W.out`, let's extract them and estimate our treatment effect. The functions `lm_weightit()` and `glm_weightit()` make it easy to fit (generalized) linear models that account for estimation of of the weights in their standard errors. We can then use functions in `marginaleffects` to perform g-computation to extract a treatment effect estimation from the outcome model.

```{r, message=FALSE}
# Fit outcome model
fit <- lm_weightit(re78 ~ treat * (age + educ + race + married +
                                     nodegree + re74 + re75),
                   data = lalonde, weightit = W.out)
```
```{r, message=FALSE, eval = me_ok}
# G-computation for the treatment effect
library("marginaleffects")
avg_comparisons(fit, variables = "treat",
                newdata = subset(treat == 1))
```

Our confidence interval for `treat` contains 0, so there isn't evidence that `treat` has an effect on `re78`. Several types of standard errors are available in `WeightIt`, including analytical standard errors that account for estimation of the weights using M-estimation, robust standard errors that treat the weights as fixed, and bootstrapping. All type are described in detail at `vignette("estimating-effects")`.

## Balancing Weights for a Longitudinal Treatment

`WeightIt` can estimate weights for longitudinal treatment marginal structural models as well. This time, we'll use the sample data set `msmdata` to estimate our weights. Data must be in "wide" format, with one row per unit.

```{r}
data("msmdata")
head(msmdata)
```

We have a binary outcome variable (`Y_B`), pre-treatment time-varying variables (`X1_0` and `X2_0`, measured before the first treatment, `X1_1` and `X2_1` measured between the first and second treatments, and `X1_2` and `X2_2` measured between the second and third treatments), and three time-varying binary treatment variables (`A_1`, `A_2`, and `A_3`). We are interested in the joint, unique, causal effects of each treatment period on the outcome. At each treatment time point, we need to achieve balance on all variables measured prior to that treatment, including previous treatments.

Using `cobalt`, we can examine the initial imbalance at each time point and overall:

```{r}
library("cobalt") #if not already attached
bal.tab(list(A_1 ~ X1_0 + X2_0,
             A_2 ~ X1_1 + X2_1 +
               A_1 + X1_0 + X2_0,
             A_3 ~ X1_2 + X2_2 +
               A_2 + X1_1 + X2_1 +
               A_1 + X1_0 + X2_0),
        data = msmdata, stats = c("m", "ks"),
        which.time = .all)
```

`bal.tab()` indicates significant imbalance on most covariates at most time points, so we need to do some work to eliminate that imbalance in our weighted data set. We'll use the `weightitMSM()` function to specify our weight models. The syntax is similar both to that of `weightit()` for point treatments and to that of `bal.tab()` for longitudinal treatments. We'll use `method = "glm"` and `stabilize = TRUE` for stabilized propensity score weights estimated using logistic regression.

```{r}
Wmsm.out <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                             A_2 ~ X1_1 + X2_1 +
                               A_1 + X1_0 + X2_0,
                             A_3 ~ X1_2 + X2_2 +
                               A_2 + X1_1 + X2_1 +
                               A_1 + X1_0 + X2_0),
                        data = msmdata, method = "glm",
                        stabilize = TRUE)
Wmsm.out
```

`weightitMSM()` estimates separate weights for each time period and then takes the product of the weights for each individual to arrive at the final estimated weights. Printing the output of `weightitMSM()` provides some details about the function call and the output. We can take a look at the quality of the weights with `summary()`, just as we could for point treatments.

```{r}
summary(Wmsm.out)
```

Displayed are summaries of how the weights perform at each time point with respect to variability. Next, we'll examine how well they perform with respect to covariate balance.

```{r}
bal.tab(Wmsm.out, stats = c("m", "ks"),
        which.time = .none)
```

By setting `which.time = .none` in `bal.tab()`, we can focus on the overall balance assessment, which displays the greatest imbalance for each covariate across time points. We can see that our estimated weights balance all covariates all time points with respect to means and KS statistics. Now we can estimate our treatment effects.

First, we fit a marginal structural model for the outcome using `glm()` with the weights included:

```{r}
# Fit outcome model
fit <- glm_weightit(Y_B ~ A_1 * A_2 * A_3 * (X1_0 + X2_0),
                    data = msmdata,
                    weightit = Wmsm.out,
                    family = binomial)
```

Then, we compute the average expected potential outcomes under each treatment regime using `marginaleffects::avg_predictions()`:

```{r, eval = me_ok}
library("marginaleffects")
(p <- avg_predictions(fit,
                      variables = c("A_1", "A_2", "A_3")))
```

We can compare the expected potential outcomes under each regime using `marginaleffects::hypotheses()`. To get all pairwise comparisons, supply the `avg_predictions()` output to `hypotheses(., ~ pairwise)`. To compare individual regimes, we can use `hypotheses()`, identifying the rows of the `avg_predictions()` output. For example, to compare the regimes with no treatment for all three time points vs. the regime with treatment for all three time points, we would run

```{r, eval = me_ok}
hypotheses(p, "b8 - b1 = 0")
```

These results indicate that receiving treatment at all time points reduces the risk of the outcome relative to not receiving treatment at all.

## References
