# Policy Learning with Decision-Theoretic Bounds

#### Deniz Akdemir

#### 2026-08-10

## Introduction

This vignette demonstrates how to use `causaldef` for safe policy learning — making treatment decisions with quantified guarantees even when unobserved confounding exists.

The key insight is the policy regret transfer bound:

\[\text{Regret}_{do}(\pi) \leq \text{Regret}_{obs}(\pi) + M \cdot \delta\]

where: - \(\text{Regret}_{do}(\pi)\) = regret under the true interventional distribution - \(\text{Regret}_{obs}(\pi)\) = regret observed in data - \(M\) = utility range (max - min possible outcomes) - \(\delta\) = deficiency (quantifies confounding)

## The Safety Floor Concept

`policy_regret_bound()` reports two complementary quantities:

- Transfer penalty \(M\cdot\delta\): additive worst-case regret inflation term, and
- Minimax safety floor \((M/2)\cdot\delta\): irreducible worst-case regret when \(\delta>0\).

If \(\delta>0\), no algorithm can guarantee zero worst-case regret without stronger assumptions or randomized data.

### Implications for AI/ML Safety

1. No algorithm can beat the safety floor: Even infinite data doesn’t help if confounding exists
2. Deficiency is the price of observational learning: To eliminate the safety floor, you need randomized experiments
3. Confidence intervals aren’t enough: Standard ML uncertainty quantification doesn’t capture confounding bias

## Practical Workflow

### Step 1: Define the Causal Problem

    library(causaldef)
    set.seed(123)

    # Simulate a treatment decision problem
    n <- 1000

    # Covariates
    age <- runif(n, 30, 70)
    severity <- rbeta(n, 2, 5) * 10

    # Confounded treatment assignment (sicker patients get treatment)
    U <- rnorm(n)  # Unmeasured health status
    ps_true <- plogis(-1 + 0.02 * age + 0.1 * severity + 0.5 * U)
    A <- rbinom(n, 1, ps_true)

    # Outcome: recovery score (0-100)
    # True effect is heterogeneous
    tau_true <- 10 + 0.2 * (age - 50)  # Older patients benefit more
    Y <- 50 + tau_true * A - 0.3 * severity + 5 * U + rnorm(n, sd = 5)

    # Clip to valid range
    Y <- pmin(100, pmax(0, Y))

    df <- data.frame(
      age = age,
      severity = severity,
      A = A,
      Y = Y
    )

### Step 2: Estimate Deficiency

    spec <- causal_spec(
      data = df,
      treatment = "A",
      outcome = "Y",
      covariates = c("age", "severity")
    )
    #> ✔ Created causal specification: n=1000, 2 covariate(s)

    # Estimate deficiency with multiple methods
    def_results <- estimate_deficiency(
      spec,
      methods = c("unadjusted", "iptw", "aipw"),
      n_boot = 100
    )
    #> ℹ Estimating deficiency: unadjusted
    #> ℹ Estimating deficiency: iptw
    #> ℹ Estimating deficiency: aipw

    print(def_results)
    #>
    #> -- Deficiency Proxy Estimates (PS-TV) ------
    #>
    #>      Method Delta     SE               CI           Quality
    #>  unadjusted 0.054 0.0139 [0.0454, 0.0965]  Caution (Yellow)
    #>        iptw 0.011 0.0042  [0.0075, 0.023] Excellent (Green)
    #>        aipw 0.011 0.0042 [0.0073, 0.0223] Excellent (Green)
    #> Note: delta is a propensity-score TV proxy (overlap/balance diagnostic).
    #>
    #> Best method: iptw (delta = 0.011 )

### Step 3: Visualize Deficiency

    plot(def_results, type = "bar")

### Step 4: Compute Policy Regret Bounds

    # Define utility range (outcome is 0-100)
    utility_range <- c(0, 100)

    # Suppose our policy achieves 5% observed regret
    obs_regret <- 5

    # Compute bound
    bounds <- policy_regret_bound(
      deficiency = def_results,
      utility_range = utility_range,
      obs_regret = obs_regret
    )
    #> Warning: Multiple fitted methods are available but `method` was not specified.
    #> ℹ Using the smallest available delta across methods is optimistic after model
    #>   selection.
    #> ℹ For a pre-specified decision bound, call `policy_regret_bound()` with `method
    #>   = '<chosen method>'`.
    #> ℹ Transfer penalty: 1.0973 (delta = 0.011)

    print(bounds)
    #>
    #> -- Policy Regret Bounds -------------------------------------------------
    #>
    #> * Deficiency delta: 0.011
    #> * Delta mode: point
    #> * Delta method: iptw
    #> * Delta selection: minimum across fitted methods
    #> * Utility range: [0, 100]
    #> * Transfer penalty: 1.0973 (additive regret upper bound)
    #> * Minimax floor: 0.5486 (worst-case lower bound)
    #>
    #> * Observed regret: 5
    #> * Interventional bound: 6.0973
    #>
    #> Note: this is a plug-in bound using a deficiency proxy rather than an identified exact deficiency.
    #> Note: minimum-across-methods selection is optimistic after model selection.
    #>
    #> Interpretation: Transfer penalty is 1.1 % of utility range given delta

### Step 5: Visualize the Safety Floor

    # Show how safety floor varies with deficiency
    plot(bounds, type = "safety_curve")

## Interpreting the Results

### The Safety Floor Report

    cat("=== Policy Deployment Decision ===\n\n")
    #> === Policy Deployment Decision ===

    delta_best <- min(def_results$estimates)
    M <- diff(utility_range)
    transfer_penalty <- M * delta_best
    minimax_floor <- 0.5 * M * delta_best

    cat(sprintf("Best achievable deficiency: %.3f\n", delta_best))
    #> Best achievable deficiency: 0.011
    cat(sprintf("Transfer penalty (M*delta): %.1f points\n", transfer_penalty))
    #> Transfer penalty (M*delta): 1.1 points
    cat(sprintf("Minimax safety floor (M/2*delta): %.1f points\n", minimax_floor))
    #> Minimax safety floor (M/2*delta): 0.5 points
    cat(sprintf("Observed regret: %.1f points\n", obs_regret))
    #> Observed regret: 5.0 points

    if (!is.null(bounds$regret_bound)) {
      cat(sprintf("Worst-case regret: %.1f points\n", bounds$regret_bound))
    }
    #> Worst-case regret: 6.1 points

    cat("\n")

    # Illustrative deployment heuristic
    if (transfer_penalty < 5) {
      cat("✓ LOW additional decision risk on the chosen 0-100 utility scale.\n")
    } else if (transfer_penalty < 10) {
      cat("⚠ MODERATE additional decision risk. Deployment may be reasonable with monitoring.\n")
    } else {
      cat("✗ HIGH additional decision risk. Consider stronger design or randomized evidence before deployment.\n")
    }
    #> ✓ LOW additional decision risk on the chosen 0-100 utility scale.

## Sensitivity Analysis with Confounding Frontiers

What if there’s additional unmeasured confounding?

    # Map the confounding frontier
    frontier <- confounding_frontier(
      spec,
      alpha_range = c(-2, 2),
      gamma_range = c(-2, 2),
      grid_size = 30
    )
    #> ℹ Computing benchmarks for observed covariates...
    #> ✔ Computed confounding frontier: 30x30 grid

    # Find an illustrative low-risk region for this utility scale
    safe_region <- subset(frontier$grid, delta < 0.1)
    cat(sprintf(
      "Illustrative low-risk region covers %.1f%% of confounding space\n",
      100 * nrow(safe_region) / nrow(frontier$grid)
    ))
    #> Illustrative low-risk region covers 100.0% of confounding space

### Visualize the Frontier

    plot(frontier, type = "heatmap", threshold = c(0.05, 0.1, 0.2))

## Policy Learning with grf (Optional)

If you have the `grf` package installed, you can use causal forests for heterogeneous treatment effect estimation with deficiency bounds:

    # Estimate deficiency using causal forests
    if (requireNamespace("grf", quietly = TRUE)) {
      def_grf <- estimate_deficiency(
        spec,
        methods = c("aipw", "grf"),
        n_boot = 50
      )

      print(def_grf)

      # Get individual treatment effect predictions
      kernel_grf <- def_grf$kernel$grf
      if (!is.null(kernel_grf$tau_hat)) {
        cat("\nHeterogeneous Effects Detected:\n")
        cat(sprintf("ATE from forest: %.2f\n", kernel_grf$ate))
        cat(sprintf("CATE range: [%.2f, %.2f]\n",
                    min(kernel_grf$tau_hat),
                    max(kernel_grf$tau_hat)))
      }
    }

## Best Practices for Safe Deployment

### Pre-Deployment Checklist

These cutoffs are illustrative for the 0–100 utility scale in this vignette. In practice, calibrate them to the decision stakes.

Check | Illustrative heuristic | Action if Failed
--- | --- | ---
Transfer penalty < 5 points | Small additional regret budget | Deployment may be reasonable
Transfer penalty in [5, 10) points | Material but possibly manageable residual risk | Deploy only with active monitoring
Transfer penalty >= 10 points | Large residual regret budget | Consider pilot RCT or narrower deployment
NC diagnostic falsified | Any | Do not deploy without more data or stronger assumptions

### Monitoring in Production

    # Example: Re-estimate deficiency on new data
    new_data <- ...  # Your production data

    new_spec <- causal_spec(
      new_data,
      treatment = "A",
      outcome = "Y",
      covariates = c("age", "severity")
    )

    # Quick check
    def_monitor <- estimate_deficiency(
      new_spec,
      methods = "iptw",
      n_boot = 50
    )

    # Alert if deficiency increased
    if (def_monitor$estimates["iptw"] > 1.5 * delta_best) {
      warning("Distribution shift detected! Deficiency increased.")
    }

## Mathematical Details

### Policy Regret Transfer (Manuscript)

For any policy \(\pi\) and bounded utility function \(u \in [0, M]\):

\[\mathbb{E}_{P^{do}}\left[\max_a u(a, X) - u(\pi(X), X)\right] \leq \mathbb{E}_{P^{obs}}\left[\max_a u(a, X) - u(\pi(X), X)\right] + M\delta\]

Proof sketch: The deficiency \(\delta\) bounds the total variation distance between the (simulated) observational and target interventional laws. Since utility is bounded by \(M\), the maximum discrepancy in expected utility is at most \(M\) times the total variation gap.

### Why This Matters

Traditional ML focuses on: - Prediction error: How well does my model predict \(Y\)? - Generalization: Does performance hold on new data?

But for causal policy learning, we need: - Interventional validity: Does my policy work when deployed? - Confounding robustness: How much could unmeasured bias hurt me?

The safety floor answers these questions with formal guarantees.

## Summary

Concept | Definition | Function
--- | --- | ---
Transfer penalty | \(M\delta\) — additive regret inflation term | `$transfer_penalty`
Minimax safety floor | \((M/2)\delta\) — irreducible worst-case regret | `$minimax_floor`
Regret bound | observed regret + transfer penalty | `$regret_bound`
Deficiency | Information gap between obs and do | `estimate_deficiency()`
Confounding Frontier | Deficiency as function of \((\alpha, \gamma)\) | `confounding_frontier()`

Use these tools to make safe, accountable decisions from observational data.

## References

1. Akdemir, D. (2026). Constraints on Causal Inference as Experiment Comparison. DOI: 10.5281/zenodo.21877511. See `thm:policy_regret` (Policy Regret Transfer) and `thm:safety_floor` (Minimax Safety Floor).

2. Athey, S., & Wager, S. (2021). Policy learning with observational data. Econometrica, 89(1), 133-161.

3. Kallus, N. (2020). Confounding-robust policy evaluation in infinite-horizon reinforcement learning. NeurIPS.