reviews: 
Comments: The paper compares ''risk-based targeting'' and ''treatment effect-based targeting'' across five datasets from RCTs in different domains. In risk-based targeting, the planner targets individuals based on their baseline risk, which is the expected outcome in the absence of treatment. This approach, commonly used by practitioners, does not account for causal effects. In contrast, treatment effect-based targeting involves first estimating the Conditional Average Treatment Effect (CATE) and then prioritizing individuals accordingly, often with the help of machine learning. A key concern is that these methods may introduce bias in the presence of unobserved confounding. The paper aims to provide empirically grounded guidance for navigating this tradeoff.

First, the paper investigates the extent to which baseline risk serves as an effective proxy for targeting. To do this, treatment effects are estimated at different levels of baseline risk, revealing a surprisingly complex and not necessarily monotonic relationship between treatment effect and baseline risk. Second, the paper examines the potential additional gains from targeting based on treatment effect. Both utilitarian and Nash welfare are used to compare these mechanisms, showing that targeting based on estimated treatment effect can be up to three times more effective in nearly all cases. This remains true even when confounding is introduced into the CATE estimation and when the policymaker's preference for risk-based targeting is encoded in the social welfare function.
Strengths: This is an important issue, and I’ve personally never found a clear guideline on how best to approach it. I found the study original and of good quality.

The paper does a commendable job of incorporating datasets from diverse domains. 

It also simulates the impact of unobserved confounders and considers the possibility of policymaker bias toward risk-based targeting.
Weaknesses: My main concern is the reliability of comparing the estimated welfare from risk-based versus treatment effect-based targeting. The issue with the latter approach, particularly in resource-scarce settings, is that it first estimates CATE, then selects individuals with the highest estimated CATE given the available budget, and finally estimates the welfare of treating these individuals using the same estimated CATE. Even if the CATE is unbiased, this procedure can lead to severely biased welfare estimates in resource-constrained settings.

In Figure 1, the paper provides treatment effect estimates for each baseline risk percentile, along with confidence intervals. What exactly are we looking for here? If the goal is to rule out a monotonic relationship between baseline risk and treatment effect, none of the figures offer statistically significant evidence to support this.

I also think the results should intuitively depend on the budget but this is not discussed in the paper. For instance, if the budget is very small, treatment effect-based allocation can be even more effective in exploiting heterogeneity. 

Overall, the writing is strong. I noticed the following minor typos: page 2 (potentially-based -> potentially-biased), page 4 (policy -> policy), page 5, second equation, page 7, Equation 4.5 is referenced which does not exist
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 3