8 Stochastic direct and indirect effects

8.1 Definition of the effects

Consider the following directed acyclic graph.

Directed acyclic graph under no intermediate confounders of the mediator-outcome relation affected by treatment

8.2 Motivation for stochastic interventions

So far we have discussed controlled, natural, and interventional (in)direct effects
These effects require that $0 < \P(A=1\mid W) < 1$
They are defined only for binary exposures
What can we do when the positivity assumption does not hold or the exposure is continuous?
Solution: We can use stochastic effects

8.3 Definition of stochastic effects

There are two possible ways of defining stochastic effects:

Consider the effect of an intervention where the exposure is drawn from a distribution
- For example incremental propensity score interventions
Consider the effect of an intervention where the post-intervention exposure is a function of the actually received exposure
- For example modified treatment policies
In both cases $A \mid W$ is a non-deterministic intervention, thus the name stochastic intervention

8.3.1 Example: incremental propensity score interventions (IPSI) (Kennedy 2018)

Kennedy, Edward H. 2018. “Nonparametric Causal Effects Based on Incremental Propensity Score Interventions.” Journal of the American Statistical Association, no. just-accepted.

Definition of the intervention

Assume $A$ is binary, and $\P(A=1\mid W=w) = g(1\mid w)$ is the propensity score
Consider an intervention in which each individual receives the intervention with probability $g_\delta(1\mid w)$, equal to \[\begin{equation*} g_\delta(1\mid w)=\frac{\delta g(1\mid w)}{\delta g(1\mid w) + 1 - g(1\mid w)} \end{equation*}\]
e.g., draw the post-intervention exposure from a Bernoulli variable with probability $g_\delta(1\mid w)$
The value $\delta$ is user given
Let $A_\delta$ denote the post-intervention exposure distribution
Some algebra shows that $\delta$ is an odds ratio comparing the pre- and post-intervention exposure distributions \[\begin{equation*} \delta = \frac{\text{odds}(A_\delta = 1\mid W=w)} {\text{odds}(A = 1\mid W=w)} \end{equation*}\]
Interpretation: what would happen in a world where the odds of receiving treatment is increased by $\delta$
Let $Y_{A_\delta}$ denote the outcome in this hypothetical world

8.3.1.1 Illustrative application for IPSIs

Consider the effect of participation in sports on children’s BMI
Mediation through snacking, exercising, etc.
Intervention: for each individual, increase the odds of participating in sports by $\delta=2$
The post-intervention exposure is a draw $A_\delta$ from a Bernoulli distribution with probability $g_\delta(1\mid w)$

Example: modified treatment policies (MTP) (Dı́az and Hejazi 2020)

Dı́az, Iván, and Nima S Hejazi. 2020. “Causal Mediation Analysis for Stochastic Interventions.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (3): 661–83.

Definition of the intervention

Consider a continuous exposure $A$ taking values in the real numbers
Consider an intervention that assigns exposure as $A_\delta = A - \delta$
Example: $A$ is pollution measured as $PM_{2.5}$ and you are interested in an intervention that reduces $PM_{2.5}$ concentration by some amount $\delta$

8.3.2 Mediation analysis for stochastic interventions

The total effect of an IPSI can be computed as a contrast of the outcome under intervention vs no intervention: \[\begin{equation*} \psi = \E[Y_{A_\delta} - Y] \end{equation*}\]
Recall the NPSEM \[\begin{align*} W & = f_W(U_W)\\ A & = f_A(W, U_A)\\ M & = f_M(W, A, U_M)\\ Y & = f_Y(W, A, M, U_Y) \end{align*}\]
From this we have \[\begin{align*} M_{A_\delta} & = f_M(W, A_\delta, U_M)\\ Y_{A_\delta} & = f_Y(W, A_\delta, M_{A_\delta}, U_Y) \end{align*}\]
Thus, we have $Y_{A_\delta} = Y_{A_\delta, M_{A_\delta}}$ and $Y = Y_{A,M_{A}}$
Let us introduce the counterfactual $Y_{A_\delta, M}$, interpreted as the outcome observed in a world where the intervention on $A$ is performed but the mediator is fixed at the value it would have taken under no intervention: [Y_{A_, M} = f_Y(W, A_, M, U_Y)]
Then we can decompose the total effect into: \[\begin{align*} \E[Y&_{A_\delta,M_{A_\delta}} - Y_{A,M_A}] = \\ &\underbrace{\E[Y_{\color{red}{A_\delta},\color{blue}{M_{A_\delta}}} - Y_{\color{red}{A_\delta},\color{blue}{M}}]}_{\text{stochastic natural indirect effect}} + \underbrace{\E[Y_{\color{blue}{A_\delta},\color{red}{M}} - Y_{\color{blue}{A},\color{red}{M}}]}_{\text{stochastic natural direct effect}} \end{align*}\]

8.4 Identification assumptions

Confounder assumptions:
- $A \indep Y_{a,m} \mid W$
- $M \indep Y_{a,m} \mid W, A$
No confounder of $M\rightarrow Y$ affected by $A$
Positivity assumptions:
- If $g_\delta(a \mid w)>0$ then $g(a \mid w)>0$
- If $\P(M=m\mid W=w)>0$ then $\P(M=m\mid A=a,W=w)>0$

Under these assumptions, stochastic effects are identified as follows

The indirect effect can be identified as follows \[\begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, W) -\E(Y\mid A=a, M, W)\}}g_\delta(a\mid W)}\right] \end{align*}\]
The direct effect can be identified as follows \[\begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, M, W) - Y\}}g_\delta(a\mid W)}\right] \end{align*}\]

Let’s dissect the formula for the indirect effect in R:

Code

n <- 1e6
w <- rnorm(n)
a <- rbinom(n, 1, plogis(1 + w))
m <- rnorm(n, w + a)
y <- rnorm(n, w + a + m)

First, fit regressions of the outcome on $(A,W)$ and $(M,A,W)$:
Code
```
fit_y1 <- lm(y ~ m + a + w)
fit_y2 <- lm(y ~ a + w)
```

Get predictions fixing $A=a$ for all possible values $a$

Code

pred_y1_a1 <- predict(fit_y1, newdata = data.frame(a = 1, m, w))
pred_y1_a0 <- predict(fit_y1, newdata = data.frame(a = 0, m, w))
pred_y2_a1 <- predict(fit_y2, newdata = data.frame(a = 1, w))
pred_y2_a0 <- predict(fit_y2, newdata = data.frame(a = 0, w))

Compute [] for each value $a$

Code

pseudo_a1 <- pred_y2_a1 - pred_y1_a1
pseudo_a0 <- pred_y2_a0 - pred_y1_a0

Estimate the propensity score $g(1\mid w)$ and evaluate the post-intervention propensity score $g_\delta(1\mid w)$

Code

pscore_fit <- glm(a ~ w, family = binomial())
pscore <- predict(pscore_fit, type = 'response')
## How do the intervention vs observed propensity score compare
pscore_delta <- 2 * pscore / (2 * pscore + 1 - pscore)

What do the post-intervention propensity scores look like?

Code

plot(pscore, pscore_delta, xlab = 'Observed prop. score',
     ylab = 'Prop. score under intervention')
abline(0, 1)

8.5 What are the odds of exposure under intervention vs real world?

Code

odds <- (pscore_delta / (1 - pscore_delta)) / (pscore / (1 - pscore))
summary(odds)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2       2       2       2       2       2

Compute the sum \[\begin{equation*} \color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, W) - \E(Y\mid A=a, M, W)\}}g_\delta(a\mid W)} \end{equation*}\]
Code
```
indirect <- pseudo_a1 * pscore_delta + pseudo_a0 * (1 - pscore_delta)
```

The average of this value is the indirect effect

Code

## E[Y(Adelta) - Y(Adelta, M)]
mean(indirect)

[1] 0.1094063

The direct effect is \[\begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, M, W) - Y\}}g_\delta(a\mid W)}\right] \end{align*}\]

Which can be computed as

Code

direct <- (pred_y1_a1 - y) * pscore_delta +
       (pred_y1_a0 - y) * (1 - pscore_delta)
mean(direct)

[1] 0.1096467

8.6 Summary

Stochastic (in)direct effects
- Relax the positivity assumption
- Can be defined for non-binary exposures
- Do not require a cross-world assumption
Still require the absence of intermediate confounders
- But, compared to the NDE and NIE, we can design a randomized study where identifiability assumptions hold, at least in principle
- There is a version of these effects that can accommodate intermediate confounders (Hejazi et al. 2022)
- R implementation to be released soon…stay tuned!

Hejazi, Nima S, Kara E Rudolph, Mark J van der Laan, and Iván Dı́az. 2022. “Nonparametric Causal Mediation Analysis for Stochastic Interventional (in) Direct Effects.” Biostatistics (in press). https://doi.org/10.1093/biostatistics/kxac002.

# Stochastic direct and indirect effects {#stochastic} ```{r} #| label: load-renv #| echo: false #| message: false renv::load(here::here()) library(here) ``` ## Definition of the effects Consider the following directed acyclic graph. ```{tikz} #| fig-cap: Directed acyclic graph under no intermediate confounders of the mediator-outcome relation affected by treatment #| echo: false \dimendef\prevdepth=0 \pgfdeclarelayer{background} \pgfsetlayers{background,main} \usetikzlibrary{arrows,positioning} \tikzset{ >=stealth', punkt/.style={ rectangle, rounded corners, draw=black, very thick, text width=6.5em, minimum height=2em, text centered}, pil/.style={ ->, thick, shorten <=2pt, shorten >=2pt,} } \newcommand{\Vertex}[2] {\node[minimum width=0.6cm,inner sep=0.05cm] (#2) at (#1) {$#2$}; } \newcommand{\VertexR}[2] {\node[rectangle, draw, minimum width=0.6cm,inner sep=0.05cm] (#2) at (#1) {$#2$}; } \newcommand{\ArrowR}[3] { \begin{pgfonlayer}{background} \draw[->,#3] (#1) to[bend right=30] (#2); \end{pgfonlayer} } \newcommand{\ArrowL}[3] { \begin{pgfonlayer}{background} \draw[->,#3] (#1) to[bend left=45] (#2); \end{pgfonlayer} } \newcommand{\EdgeL}[3] { \begin{pgfonlayer}{background} \draw[dashed,#3] (#1) to[bend right=-45] (#2); \end{pgfonlayer} } \newcommand{\Arrow}[3] { \begin{pgfonlayer}{background} \draw[->,#3] (#1) -- +(#2); \end{pgfonlayer} } \begin{tikzpicture} \Vertex{-4, 0}{W} \Vertex{0, 0}{M} \Vertex{-2, 0}{A} \Vertex{2, 0}{Y} \Arrow{W}{A}{black} \Arrow{A}{M}{black} \Arrow{M}{Y}{black} \ArrowL{W}{Y}{black} \ArrowL{A}{Y}{black} \ArrowL{W}{M}{black} \end{tikzpicture} ``` ## Motivation for stochastic interventions - So far we have discussed controlled, natural, and interventional (in)direct effects - These effects require that $0 < \P(A=1\mid W) < 1$ - They are defined only for binary exposures - _What can we do when the positivity assumption does not hold or the exposure is continuous?_ - Solution: We can use stochastic effects ## Definition of stochastic effects There are two possible ways of defining stochastic effects: - Consider the effect of an intervention where the exposure is drawn from a distribution - For example incremental propensity score interventions - Consider the effect of an intervention where the post-intervention exposure is a function of the actually received exposure - For example modified treatment policies - In both cases $A \mid W$ is a non-deterministic intervention, thus the name _stochastic intervention_ ### Example: incremental propensity score interventions (IPSI) [@kennedy2018nonparametric] {#ipsi} #### Definition of the intervention {.unnumbered} - Assume $A$ is binary, and $\P(A=1\mid W=w) = g(1\mid w)$ is the propensity score - Consider an intervention in which each individual receives the intervention with probability $g_\delta(1\mid w)$, equal to \begin{equation*} g_\delta(1\mid w)=\frac{\delta g(1\mid w)}{\delta g(1\mid w) + 1 - g(1\mid w)} \end{equation*} - e.g., draw the post-intervention exposure from a Bernoulli variable with probability $g_\delta(1\mid w)$ - The value $\delta$ is user given - Let $A_\delta$ denote the post-intervention exposure distribution - Some algebra shows that $\delta$ is an odds ratio comparing the pre- and post-intervention exposure distributions \begin{equation*} \delta = \frac{\text{odds}(A_\delta = 1\mid W=w)} {\text{odds}(A = 1\mid W=w)} \end{equation*} - Interpretation: _what would happen in a world where the odds of receiving treatment is increased by $\delta$_ - Let $Y_{A_\delta}$ denote the outcome in this hypothetical world #### Illustrative application for IPSIs - Consider the effect of participation in sports on children's BMI - Mediation through snacking, exercising, etc. - Intervention: for each individual, increase the odds of participating in sports by $\delta=2$ - The post-intervention exposure is a draw $A_\delta$ from a Bernoulli distribution with probability $g_\delta(1\mid w)$ ### Example: modified treatment policies (MTP) [@diaz2020causal] {.unnumbered} #### Definition of the intervention {.unnumbered} - Consider a continuous exposure $A$ taking values in the real numbers - Consider an intervention that assigns exposure as $A_\delta = A - \delta$ - Example: $A$ is pollution measured as $PM_{2.5}$ and you are interested in an intervention that reduces $PM_{2.5}$ concentration by some amount $\delta$ ### Mediation analysis for stochastic interventions - The total effect of an IPSI can be computed as a contrast of the outcome under intervention vs no intervention: \begin{equation*} \psi = \E[Y_{A_\delta} - Y] \end{equation*} - Recall the NPSEM \begin{align*} W & = f_W(U_W)\\ A & = f_A(W, U_A)\\ M & = f_M(W, A, U_M)\\ Y & = f_Y(W, A, M, U_Y) \end{align*} - From this we have \begin{align*} M_{A_\delta} & = f_M(W, A_\delta, U_M)\\ Y_{A_\delta} & = f_Y(W, A_\delta, M_{A_\delta}, U_Y) \end{align*} - Thus, we have $Y_{A_\delta} = Y_{A_\delta, M_{A_\delta}}$ and $Y = Y_{A,M_{A}}$ - Let us introduce the counterfactual $Y_{A_\delta, M}$, interpreted as the outcome observed in a world where the intervention on $A$ is performed but the mediator is fixed at the value it would have taken under no intervention: \[Y_{A_\delta, M} = f_Y(W, A_\delta, M, U_Y)\] - Then we can decompose the total effect into: \begin{align*} \E[Y&_{A_\delta,M_{A_\delta}} - Y_{A,M_A}] = \\ &\underbrace{\E[Y_{\color{red}{A_\delta},\color{blue}{M_{A_\delta}}} - Y_{\color{red}{A_\delta},\color{blue}{M}}]}_{\text{stochastic natural indirect effect}} + \underbrace{\E[Y_{\color{blue}{A_\delta},\color{red}{M}} - Y_{\color{blue}{A},\color{red}{M}}]}_{\text{stochastic natural direct effect}} \end{align*} ## Identification assumptions - Confounder assumptions: + $A \indep Y_{a,m} \mid W$ + $M \indep Y_{a,m} \mid W, A$ - No confounder of $M\rightarrow Y$ affected by $A$ - Positivity assumptions: + If $g_\delta(a \mid w)>0$ then $g(a \mid w)>0$ + If $\P(M=m\mid W=w)>0$ then $\P(M=m\mid A=a,W=w)>0$ Under these assumptions, stochastic effects are identified as follows - The indirect effect can be identified as follows \begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, W) -\E(Y\mid A=a, M, W)\}}g_\delta(a\mid W)}\right] \end{align*} - The direct effect can be identified as follows \begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, M, W) - Y\}}g_\delta(a\mid W)}\right] \end{align*} - Let's dissect the formula for the indirect effect in R: ```{r} n <- 1e6 w <- rnorm(n) a <- rbinom(n, 1, plogis(1 + w)) m <- rnorm(n, w + a) y <- rnorm(n, w + a + m) ``` - First, fit regressions of the outcome on $(A,W)$ and $(M,A,W)$: ```{r} fit_y1 <- lm(y ~ m + a + w) fit_y2 <- lm(y ~ a + w) ``` - Get predictions fixing $A=a$ for all possible values $a$ ```{r} pred_y1_a1 <- predict(fit_y1, newdata = data.frame(a = 1, m, w)) pred_y1_a0 <- predict(fit_y1, newdata = data.frame(a = 0, m, w)) pred_y2_a1 <- predict(fit_y2, newdata = data.frame(a = 1, w)) pred_y2_a0 <- predict(fit_y2, newdata = data.frame(a = 0, w)) ``` - Compute \[\color{ForestGreen}{\{\E(Y\mid A=a, W)-\E(Y\mid A=a, M, W)\}}\] for each value $a$ ```{r} pseudo_a1 <- pred_y2_a1 - pred_y1_a1 pseudo_a0 <- pred_y2_a0 - pred_y1_a0 ``` - Estimate the propensity score $g(1\mid w)$ and evaluate the post-intervention propensity score $g_\delta(1\mid w)$ ```{r} pscore_fit <- glm(a ~ w, family = binomial()) pscore <- predict(pscore_fit, type = 'response') ## How do the intervention vs observed propensity score compare pscore_delta <- 2 * pscore / (2 * pscore + 1 - pscore) ``` - What do the post-intervention propensity scores look like? ```{r} plot(pscore, pscore_delta, xlab = 'Observed prop. score', ylab = 'Prop. score under intervention') abline(0, 1) ``` ## What are the odds of exposure under intervention vs real world? ```{r} odds <- (pscore_delta / (1 - pscore_delta)) / (pscore / (1 - pscore)) summary(odds) ``` - Compute the sum \begin{equation*} \color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, W) - \E(Y\mid A=a, M, W)\}}g_\delta(a\mid W)} \end{equation*} ```{r} indirect <- pseudo_a1 * pscore_delta + pseudo_a0 * (1 - pscore_delta) ``` - The average of this value is the indirect effect ```{r} ## E[Y(Adelta) - Y(Adelta, M)] mean(indirect) ``` - The direct effect is \begin{align*} \E&(Y_{A_\delta} - Y_{A_\delta, M}) =\\ &\E\left[\color{Goldenrod}{\sum_{a}\color{ForestGreen}{\{\E(Y\mid A=a, M, W) - Y\}}g_\delta(a\mid W)}\right] \end{align*} - Which can be computed as ```{r} direct <- (pred_y1_a1 - y) * pscore_delta + (pred_y1_a0 - y) * (1 - pscore_delta) mean(direct) ``` ## Summary - Stochastic (in)direct effects - Relax the positivity assumption - Can be defined for non-binary exposures - Do not require a cross-world assumption - Still require the absence of intermediate confounders - But, compared to the NDE and NIE, we can design a randomized study where identifiability assumptions hold, at least in principle - There is a version of these effects that can accommodate intermediate confounders [@hejazi2020nonparametric] - `R` implementation to be released soon...stay tuned!