arxiv: 2605.12831 · v1 · submitted 2026-05-12 · 💻 cs.LG

Recognition: unknown

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

Leo Benac , Abhishek Sharma , Alihan Huyuk , Finale Doshi-Velez

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:40 UTC · model grok-4.3

classification 💻 cs.LG

keywords inverse reinforcement learningmissing observationsbehavioral datasetsoptimalityhealthcarenavigation

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The pith

Missing observations in IRL can be quantified by finding the minimal perturbations that make expert actions appear optimal.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Inverse reinforcement learning infers rewards from demonstrations, but missing observations in real data can make actions seem suboptimal. The paper develops a method to find the smallest changes to recorded observations needed to restore optimality for the expert's actions. This quantifies the potential missingness in behavioral datasets. Experiments on synthetic tasks, cancer simulators, and ICU data show the approach's utility in identifying how much unobserved information might explain the behavior.

Core claim

By identifying the minimal perturbations to the recorded observations that are needed for the expert's actions to appear optimal, the work provides a way to measure the possible extent of missing observations in IRL applications, with a practical algorithm demonstrated across multiple domains.

What carries the argument

Minimal perturbation identification to observations for restoring action optimality in IRL.

If this is right

Allows better interpretation of learned rewards in settings with incomplete data like healthcare.
Provides bounds on observation missingness without assuming specific missingness mechanisms.
Practical for real-world datasets as shown in navigation, treatment simulators, and clinical data.
Can guide data collection improvements by highlighting where observations are likely insufficient.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Applying this to other machine learning areas involving partial observability could improve model robustness.
It suggests that IRL models might need integration with missing data imputation techniques for more accurate reward inference.
In policy learning, this could inform when additional sensors or records are necessary.

Load-bearing premise

That the minimal perturbations correspond to plausible unobserved observations available to the original decision-maker.

What would settle it

Running the algorithm on data where the full observations are known but some are deliberately hidden, and checking if the recovered perturbations match the hidden parts.

Figures

Figures reproduced from arXiv: 2605.12831 by Abhishek Sharma, Alihan Huyuk, Finale Doshi-Velez, Leo Benac.

**Figure 1.** Figure 1: Suppose states consist of shapes and colors, and actions are determined solely by colors. The behavior in two episodes begins to differ after different color changes at t = 4. If the recorded data omits colors, conventional IRL cannot accurately predict actions from that time onward. Our approach provides an alternative perspective: some unobserved change at t = 4 is needed for the actions to be perfectly… view at source ↗

**Figure 2.** Figure 2: Continuous navigation tasks. Top: demonstrations; shaded boxes mark decision regions. Bottom: PCA projections of learned perturbations zn, which separate trajectories by hidden context. Evaluation metrics. We quantify potential missingness by the average size of z: 1 N PN n=1 ∥zn∥1, where ∥zn∥1 is the elementwise ℓ1 norm of the trajectory-level perturbation. Since zn enters the reward linearly, this quanti… view at source ↗

**Figure 3.** Figure 3: illustrates the mechanism in the single-decision task. The base reward explains behavior away from the center but cannot resolve the decision region, where hidden context determines whether the expert goes left or right. The learned perturbations add reward to the context-appropriate action near the decision region and penalize competing actions, so zn locally modifies the reward in the direction needed to… view at source ↗

**Figure 4.** Figure 4: Cancer simulator results. Top: Accuracy over time. Bottom: Learned kernel centers, with marker proportional to average perturbation magnitude ∥z·,k,:∥1; gray bars show bandwidth. ICU hypotension-management task. We run three experiments on the MIMIC-IV hypotension task using different observation masks: time step only, time step plus low-predictive features, and all recorded features. Because this is a rea… view at source ↗

**Figure 5.** Figure 5: Expert demonstrations for the single-decision task. The center square is the only decision [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

**Figure 6.** Figure 6: Expert demonstrations for the independent two-decision task. The first decision region [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Learned reward structure for the two-decision independent navigation task. The top row [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Expert demonstrations for the dependent two-decision task. The two decision regions are [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: Learned reward structure for the two-decision dependent navigation task. The top row [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Expert demonstrations in the cancer simulator. Each point is a monthly state from an [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗

**Figure 11.** Figure 11: ICU hypotension treatment trajectories from MIMIC-IV. Each trajectory corresponds to [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗

read the original abstract

Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants of IRL have been developed to capture complexities of human decision-making, such as subjective beliefs, imperfect planning, and dynamic goals. However, an often-overlooked issue in real-world behavioral datasets is that the recorded data may be missing observations that were available to the original decision-maker. In use-inspired settings such as healthcare, this can make expert actions appear suboptimal, even when they were near-optimal given the information available at the time. As a result, the rewards learned by standard IRL may be misleading. In this paper, we identify the minimal perturbations to the recorded observations needed for the expert's actions to appear optimal. We develop a practical algorithm for this problem and demonstrate its utility for quantifying the possible extent of missing observations in behavioral datasets through extensive experiments on synthetic navigation tasks, a cancer treatment simulator, and ICU treatment data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a practical way to flag possible missing observations in IRL by solving for minimal state perturbations that restore optimality, but the perturbations risk being meaningless without added constraints.

read the letter

The core new piece is the formulation of minimal perturbations to recorded observations so that expert actions become optimal under some recovered reward. This is distinct from prior IRL work on beliefs or noisy planning and directly targets a common real-world problem where data incompleteness makes behavior look worse than it was. They back it with an algorithm and tests on navigation tasks, a cancer simulator, and ICU records, which is a reasonable mix for showing relevance in applied settings like healthcare. That part lands as useful diagnostic thinking rather than just another theoretical extension. The main soft spot is that nothing in the setup forces the perturbations to be plausible unobserved states the expert could have seen. Without bounds on observation values or regularization that keeps changes semantically sensible, the smallest tweak that satisfies optimality can be an artifact of the metric rather than evidence of missing data. The abstract and experiments do not appear to include checks against ground-truth missingness or sensitivity analysis on how the choice of perturbation norm affects the results, so the quantification could overstate the extent of missingness in some cases. The central claim still holds as a lower-bound diagnostic if readers treat the output as a flag rather than a literal reconstruction. This paper is for people who apply IRL to noisy behavioral datasets and want a concrete tool to diagnose data quality before interpreting the learned rewards. A reader working on real-world extensions would get value from the experiments and the problem framing even if the validation needs tightening. I would send it to peer review because the practical gap is real and the approach is simple enough to iterate on with referee input.

Referee Report

2 major / 1 minor

Summary. The paper addresses missing observations in behavioral datasets used for inverse reinforcement learning (IRL). It proposes identifying the minimal perturbations to recorded observations that would render the expert's actions optimal under a recovered reward function, develops a practical algorithm for this optimization problem, and evaluates its utility for quantifying potential missingness via experiments on synthetic navigation tasks, a cancer treatment simulator, and ICU treatment data.

Significance. If the central claim holds, the work could provide a useful diagnostic for when standard IRL rewards may be misleading due to incomplete observations, with particular relevance to high-stakes domains such as healthcare. The multi-domain experimental setup is a strength, but the absence of reported derivation details, error bounds, or quantitative validation metrics in the provided description limits the assessed impact.

major comments (2)

[Abstract] Abstract: the central claim that the method quantifies the 'possible extent of missing observations' rests on the unshown algorithm and results; no derivation, convergence analysis, or validation metrics (e.g., recovery error against ground-truth missing states) are referenced, which is load-bearing for the stated contribution.
[Abstract (and implied experimental sections)] The optimization for minimal perturbations implicitly assumes that the smallest changes (in an unspecified metric) correspond to plausible unobserved observations available to the expert. Without explicit domain constraints (e.g., physiological bounds on ICU features), the recovered perturbations risk being semantically meaningless while still satisfying optimality, directly affecting interpretability of the synthetic navigation and cancer-simulator results.

minor comments (1)

Notation for the perturbation objective and the IRL recovery step should be introduced with explicit definitions to avoid ambiguity between the original and perturbed observation spaces.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We clarify that the full manuscript contains the requested details on the algorithm and experiments, and we indicate revisions to improve the abstract and discussion of assumptions.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the method quantifies the 'possible extent of missing observations' rests on the unshown algorithm and results; no derivation, convergence analysis, or validation metrics (e.g., recovery error against ground-truth missing states) are referenced, which is load-bearing for the stated contribution.

Authors: The full manuscript derives the minimal-perturbation optimization in Section 3, presents the practical algorithm with implementation details, analyzes convergence in the supplementary material, and reports quantitative validation including recovery error against ground-truth missing states on synthetic data in Section 4.1. We will revise the abstract to explicitly reference these sections. revision: yes
Referee: [Abstract (and implied experimental sections)] The optimization for minimal perturbations implicitly assumes that the smallest changes (in an unspecified metric) correspond to plausible unobserved observations available to the expert. Without explicit domain constraints (e.g., physiological bounds on ICU features), the recovered perturbations risk being semantically meaningless while still satisfying optimality, directly affecting interpretability of the synthetic navigation and cancer-simulator results.

Authors: We agree that interpretability benefits from domain constraints. The synthetic navigation experiments constrain perturbations to the valid state space by construction. The cancer-simulator experiments incorporate physiological bounds as specified in Section 4.2. For ICU data we used observed feature ranges as bounds but will add explicit discussion of this choice and its implications for semantic plausibility in the revised version. revision: partial

Circularity Check

0 steps flagged

No circularity: new optimization objective is independent of fitted inputs

full rationale

The paper formulates a new optimization problem to find minimal perturbations to recorded observations such that expert actions become optimal under a recovered reward function. This step is defined directly from the IRL optimality condition and does not reduce to any prior fitted parameter or self-citation by construction. Standard IRL is used as a building block but the perturbation quantification is an additional, independently solvable objective whose outputs are validated on synthetic navigation, cancer simulator, and ICU data rather than being tautological. No self-definitional, fitted-input-renamed-as-prediction, or load-bearing self-citation patterns appear in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on standard IRL optimality assumptions plus a new optimization for minimal perturbations; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Expert actions are optimal given complete observations
Central premise invoked to define the perturbation problem; appears in the motivation and method description.

pith-pipeline@v0.9.0 · 5471 in / 1120 out tokens · 57872 ms · 2026-05-14T19:40:05.127295+00:00 · methodology

discussion (0)

Reference graph

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