{
  "id": "2403.20250",
  "version": "v1",
  "published": "2024-03-29T15:55:06.000Z",
  "updated": "2024-03-29T15:55:06.000Z",
  "title": "Optimal Policy Learning with Observational Data in Multi-Action Scenarios: Estimation, Risk Preference, and Potential Failures",
  "authors": [
    "Giovanni Cerulli"
  ],
  "categories": [
    "stat.ML",
    "cs.AI",
    "cs.LG"
  ],
  "abstract": "This paper deals with optimal policy learning (OPL) with observational data, i.e. data-driven optimal decision-making, in multi-action (or multi-arm) settings, where a finite set of decision options is available. It is organized in three parts, where I discuss respectively: estimation, risk preference, and potential failures. The first part provides a brief review of the key approaches to estimating the reward (or value) function and optimal policy within this context of analysis. Here, I delineate the identification assumptions and statistical properties related to offline optimal policy learning estimators. In the second part, I delve into the analysis of decision risk. This analysis reveals that the optimal choice can be influenced by the decision maker's attitude towards risks, specifically in terms of the trade-off between reward conditional mean and conditional variance. Here, I present an application of the proposed model to real data, illustrating that the average regret of a policy with multi-valued treatment is contingent on the decision-maker's attitude towards risk. The third part of the paper discusses the limitations of optimal data-driven decision-making by highlighting conditions under which decision-making can falter. This aspect is linked to the failure of the two fundamental assumptions essential for identifying the optimal choice: (i) overlapping, and (ii) unconfoundedness. Some conclusions end the paper.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-03-29T15:55:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "observational data",
      "potential failures",
      "risk preference",
      "multi-action scenarios",
      "estimation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}