L9: G-Estimation

class: center, middle, inverse, title-slide

.title[
# L9: G-Estimation
]
.author[
### Jean Morrison
]
.institute[
### University of Michigan
]
.date[
### Lecture on 2025-02-24
(updated: 2025-02-24)
]

---

`$\newcommand{\ci}{\perp\!\!\!\perp}$`

## Lecture Plan

1. G-Estimation

Lecture materials from HR Chapter 14 and from Vansteelandt and Joffe (2014).

---
## G-Estimation

- This is probably the least familiar estimation method we will see for non-time varying treatments.

- G-estimation is fairly uncommon in applied lit but has several advantages:
  + For a continuous treatment, we only have to specify `$E[A \vert L]$` instead of `$f(A \vert L)$`.
  + Potentially more efficient than IP weighting.
  + Less extrapolation bias than G-computation.

- G-Estimation is also useful for time-varying data, which we will see later.

---
## Marginal Structural Models

- In IP weighting, we estimate weights `$f(A \vert L)$` and propose a **marginal structural model** (MSM) such as

`$$E[Y(a)] = \beta_0 + \beta_1 a$$`

- We then estimate the parameters of the MSM by fitting a regression, weighting each observation by `$\frac{1}{f(A_i \vert L_i)}$`.

- The model above is **marginal** because we marginalized over the distribution of `$L$`.

- We needed to get the propensity model (model for `$f(A | L)$`) right.

- We also need to get the MSM right (only hard if `$A$` is not binary).

---
## Conditional Outcome Models

- In G-Computation, we propose a model for `$E[Y \vert A, L]$`.

- This model is also structural because we are assuming that `$E[Y \vert A, L] = E[Y(a) \vert L]$`.

- However, it is a **conditional** structural model because we are conditioning on `$L$`.

---
## Semiparametric Structural Mean Models

- Suppose we have a single, continuous confounder `$L$`, a binary treatment `$A$`, and a continuous outcome `$Y$`.

- We are interested in estimating the ATE, `$E[Y(1)] - E[Y(0)]$`.

- Suppose we specify a conditional structural model,
`$$E[Y \vert A = a, L ] = E[Y(a) \vert L] = \beta_0 + \beta_1 a + \beta_2 L + \beta_3 a L$$`

- We could think of this as really specifying two structural models:
`$$E[Y(0) \vert L] = \beta_0 + \beta_2 L$$`
`$$E[Y(1) \vert L] = (\beta_0 + \beta_1) + (\beta_2 + \beta_3) L = E[Y(0) \vert L ] + \beta_1 + \beta_3L$$`
---
## Semiparametric Structural Mean Models

- Since we are only interested in the ATE, we really only care about the second formula,
`$$E[Y(1) \vert L] =  E[Y(0) \vert L ] + \beta_1 + \beta_3L$$`
- In order to estimate the ATE, we need to estimate `$\beta_1$` and `$\beta_3$`.

- The parameters in the formula for `$E[Y(0) \vert L]$` are nuisance parameters.

- If we are using G-computation, we need to estimate them, but they don't tell us anything about the causal effect.

---
## Semiparametric Structural Mean Models

- A semiparametric structural mean model only specifies a model for the target contrast (in our case difference in counterfactual means).

`$$E[Y(1) \vert  L] - E[Y(0) \vert L] = \beta_1  + \beta_3 L$$`
- This model is **semiparametric** because we don't specify a parametric form for for `$E[Y(0) \vert L]$`.

- It is **not** marginal because we are conditioning on `$L$`.

- If we know `$\beta_1$` and `$\beta_3$`, we can calculate the ATE by standardizing over `$L$`, just as we would in G-computation

`$$E[Y(1)] - E[Y(0)] = \beta_1 + \frac{1}{n}\sum_{i = 1}^n \beta_3 L_i$$`
---
## Semi-Parametric Structural Mean Models

---
## G-Estimation

- Suppose we have a semiparametric structural mean model (SMM)
$$
E[Y(a)-Y(0) \vert L] = \beta a
$$

- How do we estimate `$\beta$` if we can never observe both `$Y(a)$` and `$Y(0)$` for the same person?

---

## G-Estimation

- To motivate the method, first assume the causal effect is **homogeneous**, meaning it is exactly the same for everyone.

- This means that for each person, 
$$
Y_i(1)- Y_i(0) =  \beta
$$

- Validity of G-estimation doesn't actually require homogeneity but it is a convenient place to start.

---

## G-Estimation

- Re-write as $$ Y_i(0) = Y_i(1) - \beta$$

- By consistency, if `$A_i = a$` then `$Y_i = Y_i(a)$` so $$ Y_i(0) = Y_i - \beta A_i$$

- This means that if we know `$\beta$`, we can fill in each unit's missing counterfactual.

- Let `$$H_i(x) = Y_i - x A_i$$`

- We want to find the value of `$x$` that will make `$H_i(x)$` equal to `$Y_i(0)$`. 
  
  
  
---

## G-Estimation

- Now we will use exchangeability. Exchangeability says that 
`$Y(0) \ci A \vert L$`
aka, `$H(\beta) \ci A \vert L$`.

- For any given value of `$x$`, we can compute `$H(x)_i = Y_i - x A_i$` for every person in the study.

- We can estimate `$\beta$` as  `$\hat{\beta} = x^*$` where `$H(x^*)$` is statistically independent of `$A$` conditional on `$L$`, in our sample.

---
## Example with Categorical `$L$`

- The best estimate of `$\beta$` is the value of `$x$` that makes `$H(x)$` as close to independent of `$A \vert L$` as possible.

---
## G-Estimation

- How would we know if `$H(x)$` were independent of `$A \vert L$`?

- If we have a guess for `$x$`, we could fit a regression like
$$
logit \left( P[A = 1 \vert H(x), L] \right) = \alpha_0 + \alpha_1 H(x) + \alpha_2 L
$$

- If `$\alpha_1$` is not statistically different from 0, then our data are consistent with `$H(x) \ci A \vert L$`.

- We could find the values of `$x$` that make this true by doing a grid search, repeatedly fitting the regression for multiple candidate `$x$`'s.

- A good point estimate of `$\beta$` is the value of `$x$` that gives `$\hat{\alpha}_1(x)$` closest to 0.

---
## G-Estimation

- Alternatively, minimizing the association between `$H(x)= Y_i - xA_i$` and `$A$` conditional on `$L$` is equivalent to solving the estimating equation:

`$$\sum_{i = 1}^n  H_i(x)(A_i - E[A \vert L_i]) = 0$$`

- This is solved by

`$$x^* = \frac{\sum_{i=1}^n Y_i (A_i - E[A \vert L_i])}{\sum_{i=1}^n  A_i (A_i - E[A \vert L_i])}$$`

---
## Variance of the Estimate

- Suppose that `$\hat{x}$` is the solution to `$\min_{x} \vert \hat{\alpha}_1(x) \vert$` found via grid search.

- For every value of `$x$` we try, we get a regression fit including a `$p$`-value for `$\hat{\alpha}_1(x) = 0$`. Call this `$p$`-value `$p_1(x)$`.

- We can get a confidence interval for `$\hat{x}$` by inverting this `$p$`-value.

- The 95% confidence interval for `$\hat{x}$` is the set of values 
`$\lbrace x : p_1(x) > 0.05 \rbrace$`.

- Alternatively, we can bootstrap using the estimating equations version.

---
## Example with Categorical `$L$`

- In our categorical `$L$` example, to perform the grid search method, we first write a function to calculate `$\alpha_1(x)$`.

``` r
grid = seq(0.4, 0.6, by = 0.01)

Hx_coefs <- purrr::map_dfr(grid, function(x){
  dat$Hx <- dat$Y - x * dat$A
  # Using GEE for robust SEs
  ea_fit <- geeglm(A ~ as.factor(L) + Hx, 
                  family=binomial(), data=dat, 
                  id=id, corstr="independence")
  res <- data.frame("Est" = summary(ea_fit)$coefficients["Hx", "Estimate"], 
                    "SE" = summary(ea_fit)$coefficients["Hx", "Std.err"], 
                    "p" = summary(ea_fit)$coefficients["Hx", "Pr(>|W|)"])
})

Hx_coefs <- Hx_coefs %>% mutate(ci_lower = Est - 1.96*SE, 
                                ci_upper = Est + 1.96*SE, 
                                pns = p > 0.05)
```

---
## G-Estimation in Catgorical Example

---
## Estimating Equations in Categorical Example

``` r
gest_func <- function(dat){
  ps_model <- glm(A ~ as.factor(L),
                  family=binomial(), data=dat)
  dat$ea <- predict(ps_model, type = "response")
  est <- with(dat, sum(Y*(A-ea))/sum(A*(A -ea)))
  return(est)
}
boot_samps <- replicate(n = 500,
                       expr = {gest_func(dat[sample(seq(nrow(dat)), 
                                     size = nrow(dat), replace = TRUE),])})
est <- gest_func(dat)
round(est, digits = 2)
```

```
## [1] 0.49
```

``` r
round(est + c(-1, 1)*qnorm(0.975)*sd(boot_samps), digits = 2)
```

```
## [1] 0.45 0.52
```

---
## Beyond Homogeneous Effects

- We have motivated G-estimation under the very strong assumption that `$Y_i(1)-Y_i(0) = \beta$` for everyone.

- If this is the case, then `$H_i(\beta) = Y_i - \beta A_i$` is equal to `$Y_i(0)$`.

- However, we are only making use of mean exchangeability: `$E[Y(0) \vert L, A] = E[Y(0) \vert L]$`.

- So we only need `$H(\beta)$` to have the same conditional mean as `$Y(0)$`, i.e.
`$$E[H(\beta) \vert L] = E[Y(0) \vert L]$$`

- This is true as long as we get the semiparametric structural mean model for `$E[Y(1)]-E[Y(0)]$` correct.

---
## Censoring

- When there is censoring, we want to estimate `$E[Y(A = 1, C =0) - Y(A =0, C =0)]$`.

- We can estimate censoring weights `$W^C = \frac{1}{P[C = 0 \vert A, L]}$`, weight the population by these, and then apply G-estimation.

- Using the grid search method, this means using `$W^{C}$` as weights when we fit the model for `$E[A \vert H(x), L]$`.

- Using the estimating equations version, we add weights to the sums in numerator and denominator,

`$$x^* = \frac{\sum_{i: C_i = 0} W^C_i Y_i (A_i - E[A \vert L_i])}{\sum_{i:C_i = 0} W^C_i A_i (A_i - E[A \vert L_i])}$$`

---
## G-Estimation in NHEFS

Step 1: Compute censoring weights

``` r
dat <- read_csv("../../data/nhefs.csv") 
dat$cens <- ifelse(is.na(dat$wt82), 1, 0)

cw_fit <- glm(cens==0 ~ qsmk + sex + race + age + I(age^2) 
                     + as.factor(education) + smokeintensity + I(smokeintensity^2) 
                     + smokeyrs + I(smokeyrs^2) + as.factor(exercise) 
                     + as.factor(active) + wt71 + I(wt71^2), 
                     data = dat, family = binomial())

dat$wc <- 1/predict(cw_fit, type="response")
```

---
## Grid Search G-Estimation in NHEFS

Step 2: Estimate `$x$` by grid search.

``` r
grid = seq(2, 5, by = 0.1)

Hx_coefs <- purrr::map_dfr(grid, function(x){
  dat$Hx <- dat$wt82_71 - x * dat$qsmk
  # Using GEE for robust SEs
  gest_fit <- geeglm(qsmk ~ sex + race + age + I(age*age) + as.factor(education)
                  + smokeintensity + I(smokeintensity*smokeintensity) + smokeyrs
                  + I(smokeyrs*smokeyrs) + as.factor(exercise) + as.factor(active)
                  + wt71 + I(wt71*wt71) + Hx, 
                  family=binomial(), data=dat, weights=wc, 
                  id=seqn, corstr="independence", 
                  subset = which(!is.na(wt82_71)))
  res <- data.frame("Est" = summary(gest_fit)$coefficients["Hx", "Estimate"], 
                    "SE" = summary(gest_fit)$coefficients["Hx", "Std.err"], 
                    "p" = summary(gest_fit)$coefficients["Hx", "Pr(>|W|)"])
})

Hx_coefs <- Hx_coefs %>% mutate(ci_lower = Est - 1.96*SE, 
                                    ci_upper = Est + 1.96*SE, 
                                    pns = p > 0.05)
```

---
## G-Estimation in NHEFS

---
## Estimating Equations in NHEFS

``` r
ps_model <- glm(qsmk ~ sex + race + age + I(age*age) + as.factor(education)
                  + smokeintensity + I(smokeintensity*smokeintensity) + smokeyrs
                  + I(smokeyrs*smokeyrs) + as.factor(exercise) + as.factor(active)
                  + wt71 + I(wt71*wt71),
                  family=binomial(), data=dat)
dat$ea <- predict(ps_model, type = "response")

g_est_num <- with(dat, sum((1-cens)*wc*wt82_71*(qsmk-ea), na.rm=T))
g_est_denom <- with(dat, sum((1-cens)*wc*qsmk*(qsmk-ea)))

round(g_est_num/g_est_denom, digits = 2)
```

```
## [1] 3.51
```

---
## Assumptions

- In order for the G-estimation estimate to be consistent, we need to get two things right:

- The semiparametric structural mean model, `$$E[Y(a) \vert A = a, L] - E[Y(0) \vert A = a, L]$$`

- The model for `$E[A \vert L]$`.

---
## Effect of Positivity Violations in G-Estimation

- Suppose we have a region of covariate space with almost no treated units. In this region `$E[A \vert L]$` will be close to 0.

- In G-computation, we may get extrapolation bias because G-computation will smooth and borrow from surrounding samples to predict `$Y(1)$` for all of the untreated units in this region.  
  + This will make our estimates highly model dependent.

- On the other hand, IP weighting could have very high variance due to enormous weights for the few treated units in the region.

---
## Effect of Positivity Violations in G-Estimation

- Look again at the estimating equations version of the G-estimation estimator:

`$$x^* = \frac{\sum_{i=1}^n 1_{C_i=0}W^C_i Y_i (A_i - E[A \vert L_i])}{\sum_{i=1}^n 1_{C_i=0}W^C_i A_i (A_i - E[A \vert L_i])}$$`

-  Untreated units in the problematic region will have `$A_i - E[A \vert L_i] \approx 0$`.

- Treated units will have `$A_i - E[A \vert L_i] \approx 1$`.

- In G-estimation, the untreated units will barely contribute to the sum, so G-estimation will not suffer from extrapolation bias.
  + Treated units will contribute, but the weight of their contribution is not extreme, preventing high variance.

---
## Effect Modification

- In G-estimation, we are estimating `$E[Y(a)-Y(0) \vert L, A = a]$`, the treatment effect *conditional* on `$L$`. 
   - If there is any effect modification by variables in `$L$`, we **have** to include that in the model. 
   - To get the marginal effect, we need to standardize as in G-computation.

- This is different from the MSMs we use for IP weighting. 
  - In that case, we only put in effect modifiers if we find them interesting.

---

## Effect Modification

- Vansteelandt and Joffe argue that, in the binary `$A$` case, mis-specifying the model as `$E[Y(1) \vert L] - E[Y(0) \vert L ] = \beta_1$` when there is effect modification, still gives interpretable results.

- Suppose the true model is `$E[Y(1) \vert L] - E[Y(0) \vert L ] = \omega(L)$`

- Then the G-estimator using the incorrect model with no effect modification converges to 
`$$\frac{E\left[ Var(A \vert L)\omega(L)\right ]}{E\left [ Var(A \vert L)\right ]}$$` which is a weighted average of treatment effects, giving the most weight to strata with higher values of `$Var(A \vert L)$`, or strata with the most overlap between treated and control.

---
## Multi-Parameter Structural Mean Models

- Suppose we propose the model

$$E[Y(a)-Y(0) \vert  L] = \beta_1 a + \beta_2 a L $$

- The corresponding "H" model is

`$$H_i(x_1, x_2) = Y_i - x_1 A_i - x_2 A_i L_i$$`
- We are looking for values `$x_1^*$` and `$x_2^*$` such that `$H(x_1^*, x_2^*) \ci A \vert L$`

---
## Multi-Parameter Structural Mean Models

- We now have two parameters to estimate, so we need to perform a search over a two dimensional grid.

- At `$x_1 = \beta_1$` and `$x_2 = \beta_2$`, the `$\alpha_1$` and `$\alpha_2$` terms will both be 0.

$$
logit\left( P[A = 1 \vert H(x_1, x_2), L] \right) = \alpha_0 + \alpha_1 H(x_1, x_2) + \alpha_2 H(x_1, x_2) L + \alpha_3 L
$$

---
## Multi-Parameter Structural Mean Models

- We have some choices about how we put `$H(x_1, x_2)$` into the exposure model.

- For example, we could use

$$
logit\left( P[A = 1 \vert H(x_1, x_2), L] \right) = \alpha_0 + \alpha_1 H(x_1, x_2) + \alpha_2 H(x_1, x_2) L + \alpha_3 L
$$

$$
logit\left( P[A = 1 \vert H(x_1, x_2), L] \right) = \alpha_0 + \alpha_1 H(x_1, x_2) + \alpha_2 H(x_1, x_2) L^2 + \alpha_3 L
$$

- It turns out that the choice only affects efficiency and not consistency, and we can derive the locally most efficient choice.

---
## General Formulation

- In general we start with a structural mean model

$$g\left \lbrace E[Y(a) \vert A =a, L]\right \rbrace - g\left \lbrace E[Y(0) \vert A =a, L]\right \rbrace = \gamma(a, l ; x^*)  $$

- From this, we define

$$
`\begin{split}
&H(x) = Y - \gamma(L, A; x) \qquad &g\ \text{identity}\\
&H(x) = Y\exp[-\gamma(L, A; x)]\qquad &g\ \text{log}\\
&H(x) = expit \left( logit(E[Y \vert L, A])- \gamma(L, A; x)  \right)
) \qquad &g\ \text{logit}
\end{split}`
$$
- The fact that `$E[Y \vert L, A]$` shows up in the definition of `$H(x)$` if `$g$` is logit makes the odds ratio formulation more difficult to deal with. 
  + There is no consistent G-estimator for the causal odds ratio.

- Suppose `$x$` has dimension `$k$`.

---
## General Formulation

- Next we either grid search for a value of `$x$` such that `$H(x)$` is statistically independent of `$A - E[A \vert L]$` (possible for small `$k$`)

- Or we solve the estimating equations

`$$\sum_{i = 1}^n 1_{C_i = 0}W^C_i H_i(x)(d(A_i, L_i) - d(E[A \vert L_i], L_i)) = 0_k$$` where `$d$` is a function with that outputs a `$k$`-dimensional vector and `$0_k$` is the `$k$`-dimensional `$0$` vector.

- `$d$` can be any function of `$A$` and `$L$`, but there is a most efficient choice.

---
## Efficient choice of `$d$`

- Under homoskedasticity, meaning that the variance of `$H(x)$` is constant given `$A$` and `$L$`, the best choice of `$d$` is

$$
d(A, L) = E\left\lbrace \frac{\partial H(x)}{dx} \Big\vert A, L\right\rbrace
$$
- Under the two parameter model from a few slides ago where we had

`$$H_i(x_1, x_2) = Y_i - x_1 A_i - x_2 A_i L_{i}$$`
- The best choice of `$d$` is `$d(A,L) = \begin{pmatrix}A \\ A L_1 \end{pmatrix}$`.
  + We dropped the minus sign since we can just multiply both sides of the estimating equations by -1.

---
## Multi-Parameter Example

- In our example multi-parameter model we need to solve (dropping censoring weights for simplicity)

`$$\sum_{i = 1}^n  H_i(x) \begin{pmatrix}A_i - E[A \vert L_i]\\L_i(A_i - E[A \vert L_i]) \end{pmatrix}= \begin{pmatrix}0 \\ 0 \end{pmatrix}$$`

- Let `$D_i = A_i - E[A \vert L_i]$`.

`$$\sum_{i = 1}^n  (Y_i - x_1 A_i - x_2 A_i L_i) D_i\begin{pmatrix}1\\L_i \end{pmatrix}= \begin{pmatrix}0 \\ 0 \end{pmatrix}$$`

---
## Multi-Parameter Example

- Separating terms and rearranging : 
`$$x_1 \sum_{i = 1}^n A_i D_i\begin{pmatrix}1\\L_i \end{pmatrix} + x_2\sum_{i = 1}^n A_i L_i D_i\begin{pmatrix}1\\L_i \end{pmatrix} = \sum_{i = 1}^n  Y_i  D_i\begin{pmatrix}1\\L_i \end{pmatrix}$$`

`$$x_1 \begin{pmatrix} \sum_{i = 1}^n A_i D_i\\ \sum_{i = 1}^n A_i D_iL_i \end{pmatrix} + x_2\begin{pmatrix}\sum_{i = 1}^n A_i L_i D_i\\\sum_{i = 1}^n A_i L_i^2 D_i \end{pmatrix} = \begin{pmatrix}\sum_{i = 1}^n  Y_i  D_i\\\sum_{i = 1}^n  Y_i  D_iL_i \end{pmatrix}$$`
---
## Multi-Parameter Example

- Solving for `$(x_1, x_2)$`:

`$$\begin{pmatrix} \sum_{i = 1}^n A_i D_i & \sum_{i = 1}^n A_i D_i L_i\\ \sum_{i = 1}^n A_i D_iL_i & \sum_{i = 1}^n A_i L_i^2 D_i\end{pmatrix} \begin{pmatrix} x_1 \\ x_2 \end{pmatrix} = \begin{pmatrix}\sum_{i = 1}^n  Y_i  D_i\\\sum_{i = 1}^n  Y_i  D_iL_i \end{pmatrix}$$`
`$$\begin{pmatrix} x_1 \\ x_2 \end{pmatrix} = \begin{pmatrix} \sum_{i = 1}^n A_i D_i & \sum_{i = 1}^n A_i L_i D_i\\ \sum_{i = 1}^n A_i D_iL_i & \sum_{i = 1}^n A_i L_i^2 D_i\end{pmatrix}^{-1}\begin{pmatrix}\sum_{i = 1}^n  Y_i  D_i\\\sum_{i = 1}^n  Y_i  D_iL_i \end{pmatrix}$$`

---
## Multi-Parameter Model in NHEFS

- Suppose we believe there is effect modification by smoking intensity. Let `$L$` be smoking intensity and `$W$` the remaining confounders. We could use the SMM
`$$E[Y(a) \vert L, W] - E[Y(0) \vert L, W] = \beta_1 a + \beta_2 a L$$`

- We include `$W$` to make explicit that this model is conditional on all of the confounders. We are explicitly saying we think there is only effect modification by smoking intensity.

- For this we can use the solution to the example that we just worked out.

---
## Multi-Parameter Model in NHEFS

``` r
dat$wc[dat$cens != 0] <- 0
dat$rem_w <- with(dat, wc*(qsmk - ea))
S <- with(dat, c(sum(wt82_71*rem_w, na.rm=T), 
                 sum(smokeintensity*wt82_71*rem_w, na.rm=T)))
R <- with(dat, c(sum(qsmk*rem_w), sum(smokeintensity*qsmk*rem_w), 
                 sum(smokeintensity*qsmk*rem_w), sum(smokeintensity^2*qsmk*rem_w)))
R <- matrix(R, nrow = 2, byrow = T)
x <- solve(R) %*% S
round(x, digits = 2)
```

```
##      [,1]
## [1,] 2.92
## [2,] 0.03
```

``` r
# Standardize to get the ATE
ate <- x[1] + mean(dat$smokeintensity*x[2])
round(ate, digits = 2)
```

```
## [1] 3.54
```

---
## Doubly Robust G-Estimation

- To make the estimator more robust, we can replace `$H(x)$` with `$H(x) - E[H(x) \vert L]$`. 
  - This works using either the estimating equation approach or the grid search approach.

- To do this, we first fit an outcome model for `$E[Y \vert L, A = 0]$`.

- If `$H_i(x) = Y_i - xA_i$`, then `$H_i(x) - E[H_i(x) \vert L] = (Y_i - \hat{Y}_{i,0}) - xA_i$`.

- The resulting estimator will be consistent if either the outcome model in the untreated or the propensity model are correct.

---
## Continuous Treatments

- One nice feature of G-estimation for continuous treatments is that we only need a model for `$E[A \vert L]$`.

- For IP weighting, we need a model for `$f(A \vert L)$` which is much harder to get right.

---

## Summary

- In G-Estimation we propose a semiparametric structural mean model for the causal contrast of interest **conditional** on `$L$`, with parameters `$\boldsymbol{\beta}$`.

- We also propose a model for `$E[A \vert L]$`.

- We then obtain a function `$H(x)$` such that `$E[H(\boldsymbol{\beta}) \vert L] = E[Y(0) \vert L]$`.

- Finally, we search for the value of `$x$` that makes `$H(x)$` statistically independent of `$A \vert L$`.

---

## Summary

- G-Estimation avoids some of the problems of G-Computation and IP Weighting
  + Extrapolation bias (G-Computation)
  + Large variance (IP Weighting)
  
- The `gesttools` R package came out in 2022.
  - Primarily focused on time-varying exposures and outcomes but also works for single time point data.