Causal Inference for Decisions and Policy

Causal Decision-Making

Causal inference builds models of consequences of actions, and provides methods for learning features of those models
A natural reason why we would care about that endeavor is that we ourselves would like to take actions and understand their consequences
If the ultimate goal is to use estimates to inform actions, this should tell us which features of the model are important to learn
- It also tells us that we should evaluate our learning based on the the quality of the resulting decisions
This perspective suggests new estimands and also new ways of estimating them
To derive them, we will have to take a detour through decision theory
For an overview of causal structure in decision-making, see Pearl (2009) Ch 4.
For a recent survey of how this decision-theoretic perspective has led to procedures for learning decisions from data in economics, see Hirano and Porter (2020)
Focus today will be on general principles, with illustration (mainly) in the case of binary decisions
- Apply to question of policy targeting: who should we treat?

Decision Theory

The primitives in decision theory are a set of actions \(x\in\mathcal{X}\), a set of states \(s\in\mathcal{S}\), and the resulting consequences of those actions, which may differ by state, \(Y^x(s)\in\mathcal{Y}\)
We evaluate consequences in a particular state by a loss function \(\ell(Y^x(s),x,s):\ \mathcal{Y}\times\mathcal{X}\times\mathcal{S}\to\mathbb{R}\), such that smaller values are preferred
- Economists typically reverse the sign and call it a utility function, or if considered over a group, a welfare function
Our goal is to choose an \(x\) such that loss is small, but we do not have access to \(s\)
Instead, we have some limited amount of information, corresponding to observations \(o(s)\in\mathcal{O}\)
A policy or decision rule is a map \(x(o):\ \mathcal{O}\to\mathcal{X}\)
A “good” policy is one which makes loss \(\ell(Y^{x(o(s))}(s),x(o(s)),s)\) small, in some sense
Because loss depends on state \(s\), there is in general no unique ordering, and we have to use our subjective judgment to pick one
A criterion is a map from (a subset of) \(\{f(.):\mathcal{S}\to\mathbb{R}\}\) to \(\mathbb{R}\) applied to a loss function evaluated over a policy \(\ell(Y^{x(o(s))}(s),x(o(s)),s)\)
- Different choices lead to different branches of decision theory
Optimal choices, when they exist, minimize the criterion over the space of policies: (a subset of) \(\{x(.):\ \mathcal{O}\to\mathcal{X}\}\)
Common decision theoretic criteria:
1. Worst case loss: \(\sup_{s\in\mathcal{S}}\ell(Y^{x(o(s))}(s),x(o(s)),s)\)
- Optimization leads to minimax loss \(\inf_{x(.)}\sup_{s\in\mathcal{S}}\ell(Y^{x(o(s))}(s),x(o(s)),s)\)
- This reflects high degree of concern for performing tolerably in the very worst case, without consideration of improvements in other situations
1. Average loss: given a probability measure \(P\) over \(\mathcal{S}\), the risk of rule \(x()\) \(R_P(x(.)):=\int_{s\in\mathcal{S}}\ell(Y^{x(o(s))}(s),x(o(s)),s)dP(s)\)
- The smallest possible risk \(\inf_{x(.)}R_P(x(.))\) is called the Bayes risk, and if a rule achieves it, it can be called a Bayes policy
- This may reflect a better balance across settings, but one needs to choose the distribution carefully

Statistical Decision Theory

Split \(s\in\mathcal{S}\) into two parts: parameters \(\theta\) and variables \(\omega\), \(s=(\theta,\omega)\in(\Theta,\Omega)\), and use a criterion that treats them asymmetrically
- Why? Well, Wald (1950) said so, and he’s the guy with that clever idea about airplanes, so…
- Idea: parameters reflect fixed features of the world that we may hope to learn and rely on, while variables represent idiosyncratic variation which may best be modeled as random
A (probability) model maps parameter space to probability measures over variables \(\{P_{\theta}(\omega):\ \theta\in\Theta\}\)
A causal probability model induces a joint distribution over observed and counterfactual variables \((o(\omega,\theta),Y^{x}(\omega,\theta))\) for each \(\theta\in\Theta\)
In a Structural Causal Model (per lectures 1, 4), w.l.o.g. we can take \(\omega=(U_1,\ldots,U_p)\) and \(\theta=\{f_1,\ldots,f_p\}\), and the distribution of observables and counterfactuals can be derived by solving the original and mutilated models, respectively
We define the risk of a policy \(x()\) given parameters \(\theta\) as \(R_\theta(x()):=\int_{s\in\mathcal{S}}\ell(Y^{x(o(\omega,\theta))}(\omega,\theta),x(o(\omega,\theta)),\omega)dP_\theta(\omega)\)
If we knew \(\theta\), we could choose a rule which minimized the risk for that \(\theta\), but we don’t
So to find a decision rule, need criteria which map from (a subset of) \(\{f(.):\mathcal{\Theta}\to\mathbb{R}\}\) to \(\mathbb{R}\) applied to the risk of a policy \(R_\theta(x())\)
As before, we can consider average vs worst case
Worst case risk is \(\sup_{\theta\in\Theta}R_\theta(x(.))\), and optimal value is minimax risk \(\inf_{x()}\sup_{\theta\in\Theta}R_\theta(x(.))\)
If \(\Pi(\theta)\) is a probability measure over \(\theta\), average risk of rule \(x()\) is \(\int_{\theta\in\Theta} R_\theta(x(.))d\Pi(\theta)\)

Bayes: How

Average risk with respect to distribution \(\Pi(\theta)\) expands to \(\int_{\theta\in\Theta}\int_{\omega\in\Omega}\ell(Y^{x(o(\omega,\theta))}(\omega,\theta),x(o(\omega,\theta)),\omega)dP_\theta(\omega)d\Pi(\theta)\),
- Which is exactly risk with respect to measure \(\Pi \circ P_\theta\), a joint measure over \(s=(\theta,\omega)\)
- Therefore minimum value is Bayes risk and predictor is Bayes policy: back to symmetry
This gives us procedure for computing Bayes policy.
- Applying L.I.E., risk is \(\int_{o\in\mathcal{O}}\int_{\theta\in\Theta}\int_{\omega\in\Omega}\ell(Y^{x(o)}(\omega,\theta),x(o),\omega)dP(\omega|o,\theta)d\Pi(\theta|o)dP(o)\)
- Optimizing over rules \(x(.)\ \mathcal{O}\to\mathcal{X}\), minimum risk is achieved by minimizing conditional risk \(\underset{x(o)\in\mathcal{X}}{\inf}\int_{\theta\in\Theta}\int_{\omega\in\Omega}\ell(Y^{x(o)}(\omega,\theta),x(o),\omega)dP(\omega|o,\theta)d\Pi(\theta|o)\) for any \(o\in\mathcal{O}\)
  - Replaces optimizing over functions with optimizing over \(\mathcal{X}\) (scalar or vector)
To compute conditional risk apply Bayes rule:
- \(P(\omega|o,\theta)=P(o|\omega,\theta)P_\theta(\omega)/P(o|\theta)\) and \(\Pi(\theta|o)=P(o|\theta)\Pi(\theta)/P(o)\)
- Composition is \(P(o|\omega,\theta)P_\theta(\omega)\Pi(\theta)/P(o)=\delta_{o=o(\omega,\theta)}P_\theta(\omega)\Pi(\theta)/\int\int\delta_{o=o(\omega,\theta)}dP_\theta(\omega)d\Pi(\theta)\) since \(o\) is function of state
In practice, compute posterior by computational method (like MCMC), sample from posterior predictive distribution to get conditional risk, and then optimize over \(\mathcal{X}\)
With modern probabilistic programming languages like Stan or Turing, easy to specify distribution of parameters, then distribution of data given observables, and let modern algorithms do the sampling and posterior prediction
- It may take hours rather than seconds, but use that time to think hard about your model and objective
Example application: with loss function given by expected social welfare, parameters \(\theta\) given by response function, with Gaussian process prior, Kasy (2018) derives posterior optimal tax rate schedule

Bayes: Why

Average case risk is less pessimistic than worst case methods, and can perform especially well if prior reflects information about more important or probable outcomes
By equivalence of Bayes and putting a distribution directly over states, any prior implies an ex ante weighting on importance of states
Prior reflects subjective knowledge and judgment about features of model, in exactly the same way the likelihood does
There is also sense that even if goal is not weighted averaging with respect to an informed prior, Bayes methods may be useful
A decision rule \(x(.)\) is dominated if there is some \(x^*()\) such that \(R_\theta(x^{*}())\leq R_\theta(x())\) \(\forall\theta\in\Theta\) and \(R_\theta(x^{*}())< R_\theta(x())\) for some \(\theta\)
- A decision rule \(x(.)\) is admissible if it is not dominated
Admissibility is a Pareto-like criterion for decisions: in principle, if you care about risk, can restrict search to these rules only
- In practice, for computational reasons, these may be hard to find, so inadmissible rules used all the time
Complete class theorems state that under some regularity criteria, for any admissible rule, there is a sequence of priors for which the risk of the Bayes decision converges to the risk of that rule
- So even if concern is some other Pareto-respecting criterion like worst case risk, can consider searching among (limits of) Bayes procedures
These theorems don’t tell you what prior to use, but do narrow the search space

Relative Comparisons: (Statistical) Regret

Sometimes measure performance relative to a (possibly infeasible) benchmark of best possible decision rule if \(\theta\) were known
Let \(\mathcal{F}\) be a class of decision rules \(f(.):\ \mathcal{O}\to\mathcal{X}\) used for comparison
The Oracle Risk over class \(\mathcal{F}\) is \(\underset{f\in\mathcal{F}}{\inf}R_{\theta}(f)\) and a rule which achieves it, if one exists, is called the oracle policy \(f^{*}_\theta\)
(Statistical) regret of \(x(.)\) (in state \(s\) relative to rules \(\mathcal{F}\)) is \(R_{\theta}(x())- \underset{f\in\mathcal{F}}{\inf}R_{\theta}(f)\)
- It measures how much worse you do by not knowing the parameter \(\theta\)
Based on decomposition \(R_{\theta}(x())=\underset{f\in\mathcal{F}}{\inf}R_{\theta}(f)+(R_{\theta}(x())- \underset{f\in\mathcal{F}}{\inf}R_{\theta}(f))\), also called the “excess risk” relative to oracle
Practical justification for use is that if you are going to consider rules in \(\mathcal{F}\), best you can hope for is to control excess risk
As it is a function of parameters, bringing it to a decision requires specifying criterion
- Maximum regret \(\max_{\theta\in\Theta}\{R_{\theta}(x())- \underset{f\in\mathcal{F}}{\inf}R_{\theta}(f)\}\) more common than average

Alternate perspective: (Ex post) Regret

Even without statistical model, can compare decisions to realized outcomes.
Let \(\{s_i\}_{i=1}^{n}\in\otimes_n\mathcal{S}\) be a sequence of states and \(\{x_i(.)\}_{i=1}^{n}\) be a sequence of decision rules, called a strategy, where choice of \(x_i:\ \mathcal{O}\to\mathcal{X}\) may depend on past states
(Ex post) Regret of strategy \(\{x_i(.)\}_{i=1}^{n}\) in state \(s\) relative to rules \(\mathcal{F}\) is \(Reg_{\mathcal{F}}(\{x_i(.)\}_{i=1}^{n})(\{s_i\}_{i=1}^{n}):=\frac{1}{n}\sum_{i=1}^{n}\ell(Y^{x_i(o(s_i))}(s_i),x_i(o(s_i)),s_i)-\underset{f\in\mathcal{F}}{\inf}\frac{1}{n}\sum_{i=1}^{n}\ell(Y^{f(o(s_i))}(s),f(o(s_i)),s_i)\)
- It measures how much worse a decision was than the best possible rule in a class chosen depending on the state \(s\)
Worst case regret is \(\underset{{\{s_i\}_{i=1}^{n}\in\otimes_n\mathcal{S}}}{\sup}Reg_{\mathcal{F}}(\{x_i(.)\}_{i=1}^{n})(\{s_i\}_{i=1}^{n})\)
Strategies which achieve \(o(n)\) worst case regret (and so vanishing average regret per observation) exist under surprisingly mild conditions, allowing decisions approximately at least as good as the best of a set of comparison rules without a probability model
- Perspective has origins in game theory (Cesa-Bianchi and Lugosi (2006)), optimization (Hazan (2019)), and statistics (Shalev-Shwartz and others (2011))
Low (statistical or ex post) regret procedures do well whenever comparators do well; the catch is that they are allowed to do badly when comparators also do so

Example applications

Statistical decision theory well studied when goal is parameter estimation
- Consider \(y\sim N(\theta,1)\), observable \(y\), and decision \(x(y)\), and loss \(\ell(\theta,x(y))=(\theta-x(y))^2\)
- Choice \(x(y)\) is assumed to have no effect on outcome, so causal problem here trivial: pure prediction setting
- Risk is \(R_{\theta}(x(.))=\int(\theta-x(y))^2f(y|\theta)dy\) is MSE of estimator
Consider classical hypothesis testing problem (as in Manski (2021))
- Binary decision \(x\in\{0,1\}\) with observables \(o\) given state \(\theta\) and loss \(\ell(x,\theta)\)
- Optimal outcome would be to choose \(x=1\) when \(\ell(1,\theta)<\ell(0,\theta)\) and \(x=0\) when \(\ell(1,\theta)>\ell(0,\theta)\)
- Let \(\mathcal{E}_\theta(x) =P_\theta(x(o)=1)\) when \(\ell(1,\theta)>\ell(0,\theta)\) and \(=P_\theta(x(o)=0)\) when \(\ell(1,\theta)<\ell(0,\theta)\) be probability of error
- Risk is given by \(E_\theta[\ell(x(o),s)]= \mathcal{E}_\theta(x)\max\{\ell(1,\theta),\ell(0,\theta)\}+(1-\mathcal{E}_\theta(x))\min\{\ell(1,\theta),\ell(0,\theta)\}\)
- \(= \min\{\ell(1,\theta),\ell(0,\theta)\}+\mathcal{E}_\theta(x)|\ell(1,\theta)-\ell(0,\theta)|\)
- Regret is \(\mathcal{E}_\theta(x)|\ell(1,\theta)-\ell(0,\theta)|\): subtract off loss in case \(\theta\) were known
- Neyman Pearson hypothesis testing fixes maximum error of one kind, then minimizes other subject to that constraint
- Risk or regret criteria typically differ from fixed \(\alpha\) hypothesis test due to value of states
Example suggests that using hypothesis tests of positive average treatment effects may not be desirable way to decide whether treatment should be implemented

Finding Optimal Procedures

Given a criterion (and possibly a model), the optimal decision rule is merely a matter of computation
Except in a few settings, this computation is rarely tractable, so it is almost never done exactly
- Instead, we apply approximations and heuristics
So why go through all that work of setting up optimization problems?
- Because given a rule, we can then evaluate it by one of the above criteria, compare it to known rules, and in some cases prove near or approximate optimality
For Bayes, wide range of computational methods give approximations, whose properties will not be discussed in detail
- MCMC methods introduce sampling error, which can (usually) be decreased by running long enough
- Most importantly but often not discussed, tractability concerns may encourage use of models which do not reflect full range or set of sources of variation relevant to decision problem, such as by use of convenient parametric functional forms
- Risk of Bayes decision rules with respect to a distribution outside of the support of the prior not optimized, and may only perform well in certain cases (Müller (2013))
In settings where we have abundant data, we may use asymptotic approximations, or at least methods with guarantees that depend on sample size
By learning the parameters or distribution of variables, it is possible to approximate the risk and the optimal decision, to varying degrees of accuracy

Plug-in methods

Simplest class of methods, so ubiquitous as to be used without mentioning or naming, is to first apply some estimator \(\widehat{\theta}\) to learn parameters or features of the causal model, then plug this estimate into the decision problem, which is then solved “as if” the parameter is equal to its estimate
- Called plug-in, or “as if” optimization, or “predict-then-optimize”
Decision rule minimizing point estimate of risk is \(\widehat{x}_{\widehat{\theta}}\) such that \(R_{\widehat{\theta}}(\widehat{x}_{\widehat{\theta}}())\leq \underset{x}{\inf} R_{\widehat{\theta}}(x())+\epsilon\) for sufficiently small \(\epsilon\overset{p}{\to}0\)
When the arg min exists and is unique, equivalent to solve for the optimal decision rule as a function of parameters, then plug in
This procedure relies on (lower semi-)continuity of the minimized criterion with respect to parameters
- Sufficient conditions are given by Berge’s Theorem of the Maximum: criterion should be (lower semi-)continuous and constraint set (upper hemi-)continuous, nonempty, and compact valued in \(\theta\)
- Together with consistency of \(\widehat{\theta}\), obtain convergence of risk to minimum value
While one obtains pointwise consistency of the risk in this way, need stronger conditions for maximum or even average risk convergence
Use of a point estimate neglects uncertainty due to the fact that model parameters are not known
- May be reasonable in cases where impact of uncertainty on decision is minimal, as might be the case in large samples
Method also relies on model accurately describing optimization environment
- Under misspecification, parameter estimates may converge to best predictor under one criterion (eg squared prediction error, KL distance, etc)
- But best approximation generally not best in terms of minimizing loss of decision problem
If \(\hat{\theta}\) converges at (slow) nonparametric rates, plug in usually inherits the same
- So avoiding misspecification by flexible modeling comes at cost in terms of reduced precision
A benefit of multi-step approaches is that they allow for division of labor
An applied econometrician who does not know how their estimates will be used may want to provide a summary of the model which is informative more broadly rather than for any one task
- Andrews and Shapiro (2021) formally model this tradeoff using heterogeneous loss functions and priors across an audience

Decision Rule Asymptotics

A more principled approach to approximations might also take uncertainty into account
Intuitively, if parameter estimates are asymptotically Gaussian, under appropriate continuity conditions, decision problem should be “close to” that under Gaussian distribution
This should allow accounting for uncertainty in decisions
- Brainard (1967) principle suggests that when policy effectiveness is known only up to a noisy estimate, a risk averse policymaker may want diversify across policies exactly as in portfolio allocation problem
One can work “backwards” by finding an an optimal procedure in the limiting problem and apply it to (suitably rescaled) regular estimates in finite samples
Conditions for this to be a valid approach, in the sense of yielding comparable risk, are due to Le Cam, surveyed in Hirano and Porter (2020)
- Sufficiently well behaved asymptotically normal estimates can be analyzed in this way

Empirical optimization

As an alternative to approximating the model and its parameters, may instead just try to approximate the criterion function
For any fixed \(x()\), \(R_\theta(x())\) is a scalar functional of the model. If it is identified and regularly estimable, it may be substantially easier to estimate than the model as a whole
Let \(\phi_i(x())\) be an influence function for \(R_\theta(x())\), so that \(E_\theta[\phi_i(x())]=R_\theta(x())\)
- Denote the \(\widehat{R}(x()):=\frac{1}{n}\sum_i\phi_i(x())\) the empirical risk of rule \(x()\)
The most common example of this by far is when the variables entering the loss function are directly observable, in which case \(\phi_i(x())=\ell(Y_i^{x(o_i)},x(o_i),\omega_i)\) is the loss function itself and the empirical risk is just the sample average loss
To compare across decision rules, we can consider choosing from a set of decision rules \(\mathcal{F}\)
Empirical risk minimization chooses \(\widehat{x}^{ERM}:=\underset{x(.)\in\mathcal{F}}{\arg\min}\widehat{R}(x())\)
Under some conditions, can provide strong bounds on excess risk of ERM estimate
Excess risk \(R_{\theta}(\widehat{x}^{ERM})-\underset{x()\in\mathcal{F}}{\inf} R_\theta(x())= \stackrel{\text{Generalization Error}}{R_\theta(\widehat{x}^{ERM})-\widehat{R}(\widehat{x}^{ERM})}+\stackrel{\text{Oracle approximation}}{( \widehat{R}(\hat{x}^{ERM})-\underset{x()\in\mathcal{F}}{\inf} R_\theta(x()))}\)
- \(\leq R_\theta(\widehat{x}^{ERM})-\widehat{R}(\widehat{x}^{ERM})+\widehat{R}(x^{*})-R_\theta(x^{*})\) if \(x^*\) is value achieving infimum, because \(\widehat{x}^{ERM}\) is minimizer of empirical risk
- \(\leq 2 \underset{x\in\mathcal{F}}{\sup}|\widehat{R}(x)-R(x) |\)
Convergence of excess risk therefore implied by uniform convergence of empirical risk to population risk
Tradeoff relative to plug-in approach is that control on uniform error limits complexity of class of rules \(\mathcal{F}\) instead of class of parameters \(\Theta\)
But since function class \(\mathcal{F}\) limited, oracle risk may be high because it need not contain best unrestricted rule

Uniform convergence of risk functional

Bound on \(\underset{x\in\mathcal{F}}{\sup}|\widehat{R}(x)-R(x)|\) implies good performance of ERM
Note that relative to oracle risk, \(\widehat{R}(\widehat{x}^{ERM})\) is usually biased downwards since minimization lets it fit both true value and noise
- Uniform bounds on sample convergence limit amount by which this overfitting occurs
Convergence in probability to 0 follows from a uniform law of large numbers over class \(\mathcal{F}\)
Can show even stronger finite sample results which bound uniform error in expectation and probability
Consider \(\mathcal{F}\) such that \(\Vert f\Vert_{\infty}\leq F\) for all \(f\in \mathcal{F}\) and \(z_i\) iid
Then \(E[\underset{f\in\mathcal{F}}{\sup}\left|\frac{1}{n}\sum_{i}f(z_i)-Ef(z_i)\right|]\) is bounded, in turn, by each of the following 3 complexity measures
- Rademacher complexity: \(\frac{2}{n}E[E_\sigma[\underset{f\in\mathcal{F}}{\sup}\left|\frac{1}{n}\sum_i\sigma_if(z_i)\right||\{z_i\}_{i=1}^{n}]]\) where \(\{\sigma_i\}_{i=1}^{n}\) are a collection of iid \(\{-1,1\}\) (Rademacher) random variables. It measures expected maximum correlation with random noise
- Entropy integral: measure of the number of balls of radius \(\epsilon\) around functions \(f_i\in\mathcal{F}\) can cover set \(\mathcal{F}\), at a collection of scales \(\epsilon\)
- Vapnik-Chervonenkis (VC) dimension: measure of dimension of set of points for which all patterns can be distinguished by functions in \(\mathcal{F}\)
See Van Der Vaart and Wellner (1996), Mohri, Rostamizadeh, and Talwalkar (2018) for proofs, interpretations, and conditions which ensure complexity is bounded
For practical purposes, you can think of strongest assumption, VC dimension \(v\) finite, as covering most parametric models, and \(v\) as measuring (effective) number of parameters
- Upper bound is proportional to \(F\sqrt{\frac{v}{n}}\)
As bounds on \(\underset{x\in\mathcal{F}}{\sup}|\widehat{R}(x)-R(x) |\) hold uniformly over iid probability distributions, they do not need correct specification or any specification of probability model
Do nearly as well as best rule in class, for any distribution over the data

Application: Binary Treatment Assignment

Kitagawa and Tetenov (2018) describe empirical risk minimization for case when goal is assignment of a binary treatment \(X\) according to a rule based on covariates \(Z\) in order to maximize the average realized outcome \(Y\) among the population \(-\ell=Y^1X+Y^0(1-X)\)
- “empirical welfare maximization” \(W(x())=-R(x())=E[Y^1x(Z)+Y^0(1-x(Z))]\) since sign reversed
Policy targeting applicable when treatment may be beneficial for some populations but harmful to others
- Uses in precision medicine, aid and welfare programs, targeted educational interventions, etc
Assume exogeneity and overlap \(Y^1,Y^0\perp X|Z\), \(\epsilon\leq \pi(z)\leq 1-\epsilon\) for \(\pi(z)=P(x=1|Z=z)\)
- Then \(W(x())\) identified with (IPW) influence function \(\frac{YX}{\pi(Z)}x(Z)+\frac{Y(1-X)}{1-\pi(Z)}(1-x(Z))\)
- Standard conditioning result, under backdoor criterion for \(Z\)
Optimal decision rule if distribution known satisfies \(x(z)=1\{E[Y^1-Y^0|Z=z]\geq 0\}\)
- Plug-in requires estimating CATE, a high dimensional task likely to have slow convergence rates
Restricting to \(x()\in\mathcal{F}\) and (for now) known propensity score, \(\widehat{x}^{EWM}:=\underset{x\in\mathcal{F}}{\arg\max}\frac{1}{n}\sum_i[\frac{Y_iX_i}{\pi(Z_i)}x(Z_i)+\frac{Y_i(1-X_i)}{1-\pi(Z_i)}(1-x(Z_i))]\)
Assume \(Y\in[-M/2,M/2]\), \(\mathcal{F}\) has VC dimension \(v<\infty\)
Then strict overlap and outcome bound give boundedness, plus VC condition allow direct application of uniform bound
- \(E[\underset{x\in\mathcal{F}}{\inf}W(x)-W(\hat{x}^{ERM})]\leq C\frac{M}{\epsilon}\sqrt{\frac{v}{n}}\)
- Empirical welfare maximizer converges at parametric rates

Decision Rule Sets

Convergence requirements dictate that \(\mathcal{F}\) be a fairly simple class of rules
Vapnik Chervonenkis (VC) class of dimension \(v\) is “essentially” parametric with \(v\) parameters
Eg: linear eligibility score \(\mathcal{F}=\{1\{\sum_{j=0}^d\beta_j o_j\geq 0\}:\ \beta\in\mathbb{R}^{d+1}\}\) has VC dimension \(d+1\)
- Measure attributes, weight them by number of points, and assign treatment to those with score above a number
Decision trees of fixed depth \(L\) over \(d\) covariates are likewise a VC class with \(v=\tilde{O}(2^L\log(d))\)
- Follow ordered checklist of rules of form “if \(x_j>c_1\), check \(x_k<c_2\), else check \(x_h>c_3\),” etc
Because results are finite sample, VC dimension can grow (slowly) with sample
Rules of this form need not approximate the shape of ideal policy given by CATE
- Point of optimization is that they do as well as possible in welfare terms

Constraints on Policy Targeting

In practice, simplicity is often a virtue, since policies constrained by variety of desiderata
Budget constraint may restrict allocations to maximum fraction of population
Fairness concerns may require equalizing assignments across groups
Implementation challenges may make it hard for administrators to properly run a complex rule
- Equal pandemic UI expansions in dollar value due to limited capability to quickly change state disbursement software
Cost of data gathering may limit which variables can be used in assignment rule
Privacy concerns or potential for manipulation or misreporting may also prevent gathering some variables
- E.g., poverty surveys may use asset test based on easily observable items (like TVs, or non-dirt floors) rather than reported income
Need to avoid misreporting may be facilitated by building in incentive compatibility constraints
Political economy may limit set of rules that can be approved or sustained
Longstanding argument on “universal vs targeted benefits” hinges on many of these constraints and costs to targeting
- Specific constraints, as well as scope for welfare improvements due to heterogeneity, should determine shape of rule and degree of customization
In some applications, like web services, automated decisions may allow for much more complicated rules

Extensions

Estimated propensity scores can be handled by plugging them in to empirical maximizer formula
Regression estimates \(\mu(x,z)=E[Y|X=x,Z=z]\) can be applied by plugging into \(\underset{x\in\mathcal{F}}{\arg\max}\frac{1}{n}\sum_i[(\hat{\mu}(1,Z_i)-\hat{\mu}(0,Z_i))x(Z_i)]\)
- Note this differs from plugging CATE estimate into optimal rule
As you should be expecting by now, there is an AIPW version allowing machine learning estimates of first stage regression and propensity scores (Athey and Wager (2021))
- Letting \(\widehat{\Gamma}_i=\hat{\mu}(1,Z_i)-\hat{\mu}(0,Z_i)+\frac{X_i-\hat{\pi}(Z_i)}{\hat{\pi}(Z_i)(1-\hat{\pi}(Z_i))}(Y_i-\hat{\mu}(X_i,Z_i))\), obtain \(\widehat{x}^{DR}=\underset{x()\in\mathcal{F}}{\arg\max}\frac{1}{n}\sum_{i=1}^{n}(2x(Z_i)-1)\hat{\Gamma}_i\)
- Policy rule still required to be in class with finite VC dimension to ensure uniform bound
Similar principles apply in settings beyond binary treatment under conditional random assignment
- eg multinomial assignments Zhou, Athey, and Wager (2018), or dynamic budgets Adusumilli, Geiecke, and Schilter (2020)
In cases where effect is not point identified, decision theory still provides choices of ordering that can be used to learn policy rules
- Given an identified set \(T\subset\Theta\) maximum regret of rule \(x\) over that set \(\max_{\theta\in T}(R_{\theta}(x())-\underset{x\in\mathcal{F}}{\inf}R_{\theta}(x))\) can be calculated
- Kallus and Zhou (2018) propose to optimize treatment policies with respect to an estimate of this quantity when observed treatments assigned with confounding and so do not point identify the causal effects
- Assunção et al. (2019) apply minimax regret methods to environmental targeting with spillovers, in which effects are not point identified

Simulation Example

Apply policytree library (Sverdrup et al. (2020)) implementing Athey and Wager (2021) DR method with random forest nuisance estimators and decision tree policy class

library(policytree)
set.seed(1234)
n <- 2000
p <- 5
# Rounding down continuous covariates decreases runtime.
Z <- round(matrix(rnorm(n * p), n, p), 2)
colnames(Z) <- make.names(1:p)
X <- rbinom(n, 1, 1 / (1 + exp(Z[, 3])))
tau <- 1 / (1 + exp((Z[, 1] + Z[, 2]) / 2)) - 0.5
Y <- Z[, 3] + X * tau + rnorm(n)
c.forest <- grf::causal_forest(Z, Y, X) #Get nuisance functions
dr.scores <- double_robust_scores(c.forest) #Get scores functions
tree <- policy_tree(Z, dr.scores, 2) #Optimize over tree policies of depth 2
tree

## policy_tree object 
## Tree depth:  2 
## Actions:  1: control 2: treated 
## Variable splits: 
## (1) split_variable: X1  split_value: -0.56 
##   (2) split_variable: X3  split_value: -0.88 
##     (4) * action: 1 
##     (5) * action: 2 
##   (3) split_variable: X2  split_value: -0.72 
##     (6) * action: 2 
##     (7) * action: 1

Estimated optimal tree policy

plot(tree)

Conclusions

Decision theory offers a framework for making decisions based on data and models
- Loss function describes quality of outcome in a given state, criterion evaluates decision rules across states
Different criteria reflect tradeoffs over when and what kinds of errors are tolerated, as they inevitably must be
Decision theory paired with causal models provides ways to evaluate policies, even without knowing all features of model or environment
Approximations may be based on learning the model class or learning the policy class, either of which may be preferable in some situations

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