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Causal Analysis of Agent Behavior for AI Safety

Grégoire Déletang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega

arXiv:2103.03938v1cs.AIcs.LG

TL;DR

Powerful learning systems are opaque, and observation alone can leave causal mechanisms behind agent behavior unresolved. The report illustrates a methodology that tests causal hypotheses through carefully chosen interventions across six use cases. Its examples support human-understandable behavioral theories, while the approach depends on suitable reference knowledge for counterfactual claims and remains labor-intensive to scale.

  • Problem

    Powerful learning systems are opaque, and observational evidence can leave behavioral drivers unresolved when behavior may reflect confounding or subtle memory strategies.

    Source: §1 · p. 1, §3.1 · p. 5, §3.2 · p. 6

  • Method

    The report illustrates causal analysis by formulating behavioral hypotheses, probing agents with chosen interventions, and validating causal models across six use cases.

    Source: §3.5 · pp. 10–11, §4 · p. 14

  • Results

    Across six examples, the report shows that mechanistic knowledge required direct interaction through interventions and that validated theories can use human-understandable concepts rather than neural details.

    Source: §4 · p. 14

  • Takeaways & Limitations

    Analysts can choose explanation detail, using general models for overall behavior and more detailed models for specific cases.

    Source: §4 · p. 14

    Counterfactual claims require a suitable reference agent population, and proposing hypotheses and experiments is labor-intensive with unclear scalability.

    Source: §3.4 · p. 10, §4 · p. 14

Abstract

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As machine learning systems become more powerful they also become increasingly unpredictable and opaque. Yet, finding human-understandable explanations of how they work is essential for their safe deployment. This technical report illustrates a methodology for investigating the causal mechanisms that drive the behaviour of artificial agents. Six use cases are covered, each addressing a typical question an analyst might ask about an agent. In particular, we show that each question cannot be addressed by pure observation alone, but instead requires conducting experiments with systematically chosen manipulations so as to generate the correct causal evidence.

1. Introduction

Powerful learning systems can be opaque because their components interact in complex ways, making behavior difficult to predict. The report proposes studying agents through causal experiments that validate human-understandable explanations of their behavior.

1. Introduction

  • Opaque behavior in powerful learning systems makes human-understandable explanations important for safe deployment.The report notes that poorly understood systems lack necessary safety guarantees for deployment.Source: §1 · p. 1
  • Causal experiments test hypothesized cause-effect structures and favor high-level explanations that abstract away from neural implementation details.The proposed explanations describe why an agent acts as it does.Source: §1 · p. 1
  • A simulator supports controlled inputs and interventions, while a causal reasoning engine lets analysts encode assumptions and validate hypotheses.The human-in-the-loop approach is labor-intensive but produces mechanistic explanations understandable to people.Source: §1 · p. 1

2. Methodology

The methodology combines an agent-environment simulator with a causal reasoning engine so analysts can investigate behavior through controlled experiments. Analysts probe interactions, build and query causal models, and validate their hypotheses against simulated evidence.

2. Methodology

  • The report illustrates its methodology with six use cases spanning prototypical questions about mechanistic drivers of agent behavior.The examples focus on environmental and behavioral manipulations, while noting that internal-state interventions are also possible.Source: §2 · pp. 1–2

2.1. Agents and environments

  • The methodology uses stateful agents and environments that exchange actions and observations over discrete time steps.Their internal memory and world states evolve according to the interaction symbols.Source: §2.1 · p. 2, Figure 1 · p. 2

2.2. Simulator

  • A simulator generates interaction traces for a coupled agent and environment, and its deterministic reparameterization makes random elements explicit.The simulator can rewind or expand traces and alter seeds or states to sample new trajectories or branch from a shared root.Source: §2.2 · p. 2, §2.1 · p. 2

2.3. Causal reasoning engine

  • The causal reasoning engine lets analysts state and validate hypotheses through association, intervention, and counterfactual queries.Interventions replace a variable’s structural equation with a constant, removing its modeled dependency on its prior causes.Source: §2.3 · pp. 3–4
  • Analysts specify structural causal models over selected variables, with functions and exogenous variables inducing the model’s conditional probabilities.Probabilities may be supplied directly or estimated from Monte-Carlo samples.Source: §2.3 · p. 3

2.4. Analysis workflow

  • Analysts probe trained agents with simulator interventions, formulate causal models, estimate unknown probabilities from Monte-Carlo rollouts, and verify model queries empirically.Interventions that create branches through transitions with zero probability require separate probability estimates for the resulting subtrees.Source: §2.4 · pp. 4–5

3. Experiments

The experiments use causal queries and interventions to investigate agent behavior and mechanisms that observation alone cannot establish, including under black-box conditions. They also address the reference populations needed for counterfactual claims and how pathway analysis can distinguish decision mechanisms.

3. Experiments

  • The section presents six mechanistic investigations under black-box conditions, where analysts lack trainer information but can consult pretrained stock agents.The experiments use gridworld environments and recurrent deep reinforcement learning agents.Source: §3 · p. 5

3.1. Causal effects under confounding

  • Interventions showed agent A followed floor type while B followed reward location, despite both solving the observed task, exposing behavior that observation alone could not diagnose.Manipulating the intended criterion—the reward location—revealed the difference; identifying the confounder can be harder.Source: Table 1 · p. 6, §3.1 · p. 6

3.2. Memory

  • After a mid-trajectory push, agent a still reached the cue-indicated terminal, whereas agent b lost track, revealing internal-memory use versus external-memory off-loading.Both agents appeared successful in observational rollouts, so the intervention exposed a distinction that ordinary behavior did not establish.Source: §3.2 · pp. 6–8, Table 2 · p. 8

3.3. Robust generalization

  • Interventions across reward locations found agent A successful throughout, while agent B performed best in the southern quadrant and declined as distance from it increased.The study approximates robust generalization by subdividing the target class and estimating success in each part, since testing every instance is infeasible.Source: §3.3 · pp. 8–9, Table 3 · p. 9

3.4. Counterfactuals

  • For counterfactuals without a usable simulator, the analysts modeled agent type and open gate using prior-trained reward-seeking agents to estimate behavior under an alternate gate.These model-based counterfactuals rely on prior knowledge about similar agents and do not make empirically verifiable claims.Source: §3.4 · p. 9
  • After observing an agent pick up a red reward (R = re) with the left gate open (D = l), the model assigns 0.9960 probability to the agent being red-loving (A = re).Source: §3.4 · p. 10
  • Meaningful counterfactual claims about an individual agent require prior knowledge of a suitable reference agent population; without a reference class, such claims cannot be made meaningfully.A suitable reference class may come from a distribution of agents with previous data or reasonable priors.Source: §3.4 · p. 10

3.5. Causal induction

  • After forcing the red agent left, observing the blue agent move right supports blue as the likely leader, whereas observation alone provides no evidence favoring either causal hypothesis.Without intervention, the causal hypotheses remained observationally indistinguishable.Source: §3.5 · p. 12
  • Causal induction compares competing mechanistic hypotheses by selecting interventions that make their observation likelihoods unequal; in the mimic task, observation alone cannot identify the leader.The two agents move simultaneously, and the imitator matches the leader except for random actions 10% of the time.Source: §3.5 · pp. 11–12

3.6. Causal pathways

  • The difference was 0.2338 for agent A and 0.0014 ≈0 for agent B, implying A uses D →K →R, whereas B uses only K →R.Source: §3.6 · p. 13

4. Discussion and Conclusions

The report presents causal analysis as a way to investigate agent behavior through interventions, with six examples illustrating how analysts can formulate and test behavioral theories. It also discusses the method’s scope, its human-readable explanations, and practical limits.

Related work

  • The analysis of black-box behavior has roots in circuit theory and cybernetics, while formal reasoning about causal manipulations and statistical evidence developed more recently (Dawid, 2015).Related work includes causal incentive diagrams for analyzing decision pathways (Everitt et al., 2019; Carey et al., 2020), alongside predominantly white-box approaches and a black-box observational approach (Rabinowitz et al., 2018).Source: §4 · p. 13

Scope

  • The methodology is framed as applicable beyond agents interacting with artificial environments, including sequence predictors, language models, speech synthesizers, and multi-agent systems.The report notes that choosing suitable hypotheses, abstractions, and interventions may be more challenging for model systems.Source: §4 · pp. 13–14

Features and limitations

  • Choosing suitable hypotheses and experiments requires ingenuity and can be labor-intensive, while scaling the process through automation remains unclear.The report notes that visually spotting a confounder is not generally viable and that proposing causal orderings distinct from sampled trajectory order can be non-trivial.Source: §4 · p. 14
  • The method yields human-explainable theories proposed and validated by analysts, using simplified model variables rather than the agents’ underlying neuronal activations.Analysts can choose the level of detail, from general behavior models to models for specific cases.Source: §4 · p. 14
  • The report leaves systematic uncertainty quantification for future work, noting that probability estimates from limited Monte Carlo samples may deviate from true values and could underestimate failure-mode probabilities.It recommends confidence intervals to assess estimate reliability and risk factors; this work reports sample counts instead.Source: §4 · p. 14

Conclusions and outlook

  • Six examples show analysts form causal hypotheses, test them with carefully chosen interventions, and check predictions from causal models; the report stresses that observation alone cannot provide this mechanistic knowledge.The authors also report benefiting from an automated causal reasoning engine because interpreting causal evidence is difficult.Source: §4 · p. 14

A. Architecture and training details

The report describes a convolutional and recurrent agent architecture, trained with Impala policy gradients and randomized environment and agent seeds.

A. Architecture and training details

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