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[Workshop] Targeted Learning in the tlverse: Causal Inference Meets Machine Learning

2 The Roadmap for Targeted Learning

2.1 Learning Objectives

By the end of this chapter you will be able to:

  1. Translate scientific questions to statistical questions.
  2. Define a statistical model based on the knowledge of the experiment that generated the data.
  3. Identify a causal parameter as a function of the observed data distribution.
  4. Explain the following causal and statistical assumptions and their implications: i.i.d., consistency, interference, positivity, SUTVA.

2.2 Introduction

The roadmap of statistical learning is concerned with the translation from real-world data applications to a mathematical and statistical formulation of the relevant estimation problem. This involves data as a random variable having a probability distribution, scientific knowledge represented by a statistical model, a statistical target parameter representing an answer to the question of interest, and the notion of an estimator and sampling distribution of the estimator.

2.3 The Roadmap

Following the roadmap is a process of five stages.

  1. Data as a random variable with a probability distribution, \(O \sim P_0\).
  2. The statistical model \(\mathcal{M}\) such that \(P_0 \in \mathcal{M}\).
  3. The statistical target parameter \(\Psi\) and estimand \(\Psi(P_0)\).
  4. The estimator \(\hat{\Psi}\) and estimate \(\hat{\Psi}(P_n)\).
  5. A measure of uncertainty for the estimate \(\hat{\Psi}(P_n)\).

(1) Data as a random variable with a probability distribution, \(O \sim P_0\)

The data set we’re confronted with is the result of an experiment and we can view the data as a random variable, \(O\), because if we repeat the experiment we would have a different realization of this experiment. In particular, if we repeat the experiment many times we could learn the probability distribution, \(P_0\), of our data. So, the observed data \(O\) with probability distribution \(P_0\) are \(n\) independent identically distributed (i.i.d.) observations of the random variable \(O; O_1, \ldots, O_n\). Note that while not all data are i.i.d., there are ways to handle non-i.i.d. data, such as establishing conditional independence, stratifying data to create sets of identically distributed data, etc. It is crucial that researchers be absolutely clear about what they actually know about the data-generating distribution for a given problem of interest. Unfortunately, communication between statisticians and researchers is often fraught with misinterpretation. The roadmap provides a mechanism by which to ensure clear communication between research and statistician – it truly helps with this communication!

The empirical probability measure, \(P_n\)

Once we have \(n\) of such i.i.d. observations we have an empirical probability measure, \(P_n\). The empirical probability measure is an approximation of the true probability measure \(P_0\), allowing us to learn from our data. For example, we can define the empirical probability measure of a set, \(A\), to be the proportion of observations which end up in \(A\). That is, \[\begin{equation*} P_n(A) = \frac{1}{n}\sum_{i=1}^{n} \mathbb{I}(O_i \in A) \end{equation*}\]

In order to start learning something, we need to ask “What do we know about the probability distribution of the data?” This brings us to Step 2.

(2) The statistical model \(\mathcal{M}\) such that \(P_0 \in \mathcal{M}\)

The statistical model \(\mathcal{M}\) is defined by the question we asked at the end of \(\ref{step1}\). It is defined as the set of possible probability distributions for our observed data. Often \(\mathcal{M}\) is very large (possibly infinite-dimensional), to reflect the fact that statistical knowledge is limited. In the case that \(\mathcal{M}\) is infinite-dimensional, we deem this a nonparametric statistical model.

Alternatively, if the probability distribution of the data at hand is described by a finite number of parameters, then the statistical model is parametric. In this case, we prescribe to the belief that the random variable \(O\) being observed has, e.g., a normal distribution with mean \(\mu\) and variance \(\sigma^2\). More formally, a parametric model may be defined \[\begin{equation*} \mathcal{M} = \{P_{\theta} : \theta \in \mathcal{R}^d \} \end{equation*}\]

Sadly, the assumption that the data-generating distribution has a specific, parametric forms is all-too-common, even when such is a leap of faith. This practice of oversimplification in the current culture of data analysis typically derails any attempt at trying to answer the scientific question at hand; alas, such statements as the ever-popular quip of Box that “All models are wrong but some are useful,” encourage the data analyst to make arbitrary choices even when that often force significant differences in answers to the same estimation problem. The Targeted Learning paradigm does not suffer from this bias since it defines the statistical model through a representation of the true data-generating distribution corresponding to the observed data.

Now, on to Step 3: ``What are we trying to learn from the data?“

(3) The statistical target parameter \(\Psi\) and estimand \(\Psi(P_0)\)

The statistical target parameter, \(\Psi\), is defined as a mapping from the statistical model, \(\mathcal{M}\), to the parameter space (i.e., a real number) \(\mathcal{R}\). That is, \(\Psi: \mathcal{M}\rightarrow\mathbb{R}\). The target parameter may be seen as a representation of the quantity that we wish to learn from the data, the answer to a well-specified (often causal) question of interest. In contrast to purely statistical target parameters, causal target parameters require identification from the observed data, based on causal models that include several untestable assumptions, described in more detail in the section on causal target parameters.

For a simple example, consider a data set which contains observations of a survival time on every subject, for which our question of interest is “What’s the probability that someone lives longer than five years?” We have, \[\begin{equation*} \Psi(P_0) = \mathbb{P}(O > 5) \end{equation*}\]

This answer to this question is the estimand, \(\Psi(P_0)\), which is the quantity we’re trying to learn from the data. Once we have defined \(O\), \(\mathcal{M}\) and \(\Psi(P_0)\) we have formally defined the statistical estimation problem.

(4) The estimator \(\hat{\Psi}\) and estimate \(\hat{\Psi}(P_n)\)

To obtain a good approximation of the estimand, we need an estimator, an a priori-specified algorithm defined as a mapping from the set of possible empirical distributions, \(P_n\), which live in a non-parametric statistical model, \(\mathcal{M}_{NP}\) (\(P_n \in \mathcal{M}_{NP}\)), to the parameter space of the parameter of interest. That is, \(\hat{\Psi} : \mathcal{M}_{NP} \rightarrow \mathbb{R}^d\). The estimator is a function that takes as input the observed data, a realization of \(P_n\), and gives as output a value in the parameter space, which is the estimate, \(\hat{\Psi}(P_n)\).

Where the estimator may be seen as an operator that maps the observed data and corresponding empirical distribution to a value in the parameter space, the numerical output that produced such a function is the estimate. Thus, it is an element of the parameter space based on the empirical probability distribution of the observed data. If we plug in a realization of \(P_n\) (based on a sample size \(n\) of the random variable \(O\)), we get back an estimate \(\hat{\Psi}(P_n)\) of the true parameter value \(\Psi(P_0)\).

In order to quantify the uncertainty in our estimate of the target parameter (i.e., to construct statistical inference), an understanding of the sampling distribution of our estimator will be necessary. This brings us to Step 5.

(5) A measure of uncertainty for the estimate \(\hat{\Psi}(P_n)\)

Since the estimator \(\hat{\Psi}\) is a function of the empirical distribution \(P_n\), the estimator itself is a random variable with a sampling distribution. So, if we repeat the experiment of drawing \(n\) observations we would every time end up with a different realization of our estimate and our estimator has a sampling distribution. The sampling distribution of an estimator can be theoretically validated to be approximately normally distributed by a Central Limit Theorem (CLT).

A class of Central Limit Theorems (CLTs) are statements regarding the convergence of the sampling distribution of an estimator to a normal distribution. In general, we will construct estimators whose limit sampling distributions may be shown to be approximately normal distributed as sample size increases. For large enough \(n\) we have, \[\begin{equation*} \hat{\Psi}(P_n) \sim N \left(\Psi(P_0), \frac{\sigma^2}{n}\right), \end{equation*}\] permitting statistical inference. Now, we can proceed to quantify the uncertainty of our chosen estimator by construction of hypothesis tests and confidence intervals. For example, we may construct a confidence interval at level \((1 - \alpha)\) for our estimand, \(\Psi(P_0)\): \[\begin{equation*} \hat{\Psi}(P_n) \pm z_{1 - \frac{\alpha}{2}} \left(\frac{\sigma}{\sqrt{n}}\right), \end{equation*}\] where \(z_{1 - \frac{\alpha}{2}}\) is the \((1 - \frac{\alpha}{2})^\text{th}\) quantile of the standard normal distribution. Often, we will be interested in constructing 95% confidence intervals, corresponding to mass \(\alpha = 0.05\) in either tail of the limit distribution; thus, we will typically take \(z_{1 - \frac{\alpha}{2}} \approx 1.96\).

Note: we will typically have to estimate the standard error, \(\frac{\sigma}{\sqrt{n}}\).

A 95% confidence interval means that if we were to take 100 different samples of size \(n\) and compute a 95% confidence interval for each sample then approximately 95 of the 100 confidence intervals would contain the estimand, \(\Psi(P_0)\). More practically, this means that there is a 95% probability (or 95% confidence) that the confidence interval procedure will contain the true estimand. However, any single estimated confidence interval either will contain the true estimand or will not.

2.4 Summary of the Roadmap

Data, \(O\), is viewed as a random variable that has a probability distribution. We often have \(n\) units of independent identically distributed units with probability distribution \(P_0\) (\(O_1, \ldots, O_n \sim P_0\)). We have statistical knowledge about the experiment that generated this data. In other words, we make a statement that the true data distribution \(P_0\) falls in a certain set called a statistical model, \(\mathcal{M}\). Often these sets are very large because statistical knowledge is very limited so these statistical models are often infinite dimensional models. Our statistical query is, “What are we trying to learn from the data?” denoted by the statistical target parameter, \(\Psi\), which maps the \(P_0\) into the estimand, \(\Psi(P_0)\). At this point the statistical estimation problem is formally defined and now we will need statistical theory to guide us in the construction of estimators. There’s a lot of statistical theory we will review in this course that, in particular, relies on the Central Limit Theorem, allowing us to come up with estimators that are approximately normally distributed and also allowing us to come with statistical inference (i.e., confidence intervals and hypothesis tests).

2.5 Causal Target Parameters

2.5.1 The Causal Model

After formalizing the data and the statistical model, we can define a causal model to express causal parameters of interest. Directed acyclic graphs (DAGs) are one useful tool to express what we know about the causal relations among variables. Ignoring exogenous \(U\) terms (explained below), we assume the following ordering of the variables in the observed data \(O\).

While directed acyclic graphs (DAGs) like above provide a convenient means by which to visualize causal relations between variables, the same causal relations among variables can be represented via a set of structural equations, which define the non-parametric structural equation model (NPSEM): \[\begin{align*} W &= f_W(U_W) \\ A &= f_A(W, U_A) \\ Y &= f_Y(W, A, U_Y), \end{align*}\] where \(U_W\), \(U_A\), and \(U_Y\) represent the unmeasured exogenous background characteristics that influence the value of each variable. In the NPSEM, \(f_W\), \(f_A\) and \(f_Y\) denote that each variable (for \(W\), \(A\) and \(Y\), respectively) is a function of its parents and unmeasured background characteristics, but note that there is no imposition of any particular functional constraints(e.g., linear, logit-linear, only one interaction, etc.). For this reason, they are called non-parametric structural equation models (NPSEMs). The DAG and set of nonparametric structural equations represent exactly the same information and so may be used interchangeably.

The first hypothetical experiment we will consider is assigning exposure to the whole population and observing the outcome, and then assigning no exposure to the whole population and observing the outcome. On the nonparametric structural equations, this corresponds to a comparison of the outcome distribution in the population under two interventions:

  1. \(A\) is set to \(1\) for all individuals, and
  2. \(A\) is set to \(0\) for all individuals.

These interventions imply two new nonparametric structural equation models. For the case \(A = 1\), we have \[\begin{align*} W &= f_W(U_W) \\ A &= 1 \\ Y(1) &= f_Y(W, 1, U_Y), \end{align*}\] and for the case \(A=0\), \[\begin{align*} W &= f_W(U_W) \\ A &= 0 \\ Y(0) &= f_Y(W, 0, U_Y). \end{align*}\]

In these equations, \(A\) is no longer a function of \(W\) because we have intervened on the system, setting \(A\) deterministically to either of the values \(1\) or \(0\). The new symbols \(Y(1)\) and \(Y(0)\) indicate the outcome variable in our population if it were generated by the respective NPSEMs above; these are often called counterfactuals (since they run contrary-to-fact). The difference between the means of the outcome under these two interventions defines a parameter that is often called the “average treatment effect” (ATE), denoted \[\begin{equation}\label{eqn:ate} ATE = \mathbb{E}_X(Y(1)-Y(0)), \end{equation}\] where \(\mathbb{E}_X\) is the mean under the theoretical (unobserved) full data \(X = (W, Y(1), Y(0))\).

Note, we can define much more complicated interventions on NPSEM’s, such as interventions based upon rules (themselves based upon covariates), stochastic rules, etc. and each results in a different targeted parameter and entails different identifiability assumptions discussed below.

2.5.2 Identifiability

Because we can never observe both \(Y(0)\) (the counterfactual outcome when \(A=0\)) and \(Y(1)\) (similarly, the counterfactual outcome when \(A=1\)), we cannot estimate \(\ref{eqn:ate}\) directly. Instead, we have to make assumptions under which this quantity may be estimated from the observed data \(O \sim P_0\) under the data-generating distribution \(P_0\). Fortunately, given the causal model specified in the NPSEM above, we can, with a handful of untestable assumptions, estimate the ATE, even from observational data. These assumptions may be summarized as follows

  1. The causal graph implies \(Y(a) \perp A\) for all \(a \in \mathcal{A}\), which is the randomization assumption. In the case of observational data, the analogous assumption is strong ignorability or no unmeasured confounding \(Y(a) \perp A \mid W\) for all \(a \in \mathcal{A}\);
  2. Although not represented in the causal graph, also required is the assumption of no interference between units, that is, the outcome for unit \(i\) \(Y_i\) is not affected by exposure for unit \(j\) \(A_j\) unless \(i=j\);
  3. Consistency of the treatment mechanism is also required, i.e., the outcome for unit \(i\) is \(Y_i(a)\) whenever \(A_i = a\), an assumption also known as “no other versions of treatment”;
  4. It is also necessary that all observed units, across strata defined by \(W\), have a bounded (non-deterministic) probability of receiving treatment – that is, \(0 < \mathbb{P}(A = a \mid W) < 1\) for all \(a\) and \(W\)). This assumption is referred to as positivity or overlap.

Remark: Together, (2) and (3), the assumptions of no interference and consistency, respectively, are jointly referred to as the stable unit treatment value assumption (SUTVA).

Given these assumptions, the ATE may be re-written as a function of \(P_0\), specifically \[\begin{equation}\label{eqn:estimand} ATE = \mathbb{E}_0(Y(1) - Y(0)) = \mathbb{E}_0 \left(\mathbb{E}_0[Y \mid A = 1, W] - \mathbb{E}_0[Y \mid A = 0, W]\right), \end{equation}\] or the difference in the predicted outcome values for each subject, under the contrast of treatment conditions (\(A = 0\) vs. \(A = 1\)), in the population, averaged over all observations. Thus, a parameter of a theoretical “full” data distribution can be represented as an estimand of the observed data distribution. Significantly, there is nothing about the representation in \(\ref{eqn:estimand}\) that requires parameteric assumptions; thus, the regressions on the right hand side may be estimated freely with machine learning. With different parameters, there will be potentially different identifiability assumptions and the resulting estimands can be functions of different components of \(P_0\).