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Agrin Hilmkil

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7 papers
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7

TMLR Journal 2025 Journal Article

Amortized Inference of Causal Models via Conditional Fixed-Point Iterations

  • Divyat Mahajan
  • Jannes Gladrow
  • Agrin Hilmkil
  • Cheng Zhang
  • Meyer Scetbon

Structural Causal Models (SCMs) offer a principled framework to reason about interventions and support out-of-distribution generalization, which are key goals in scientific discovery. However, the task of learning SCMs from observed data poses formidable challenges, and often requires training a separate model for each dataset. In this work, we propose an amortized inference framework that trains a single model to predict the causal mechanisms of SCMs conditioned on their observational data and causal graph. We first use a transformer-based architecture for amortized learning of dataset embeddings, and then extend the Fixed-Point Approach (FiP) to infer the causal mechanisms conditionally on their dataset embeddings. As a byproduct, our method can generate observational and interventional data from novel SCMs at inference time, without updating parameters. Empirical results show that our amortized procedure performs on par with baselines trained specifically for each dataset on both in and out-of-distribution problems, and also outperforms them in scare data regimes.

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RLC Conference 2025 Conference Paper

AVID: Adapting Video Diffusion Models to World Models

  • Marc Rigter
  • Tarun Gupta
  • Agrin Hilmkil
  • Chao Ma

Reinforcement learning (RL) is highly effective in domains that can be easily simulated. However, in problems such as robotic manipulation, accurate simulation is challenging and gathering large amounts of real-world data is impractical. A potential solution lies in leveraging widely-available unlabelled videos to train world models that simulate the consequences of actions. If the world model is accurate, it can be used to generate synthetic data to optimize decision-making via RL. Image-to-video diffusion models are already capable of generating highly realistic synthetic videos. However, these models are not action-conditioned, and the most powerful models are closed-source which means they cannot be finetuned. In this work, we propose to adapt pretrained video diffusion models to action-conditioned world models, without access to the parameters of the pretrained model. Our approach, AVID, trains an adapter on a small domain-specific dataset of action-labelled videos. AVID uses a learned mask to modify the intermediate outputs of the pretrained model and generate accurate action-conditioned videos. We evaluate AVID on video game and real-world robotics data, and show that it generally outperforms baselines for diffusion adaptation in video and image metrics. AVID demonstrates that pretrained video models have the potential to be powerful tools for generating synthetic data for RL agents. In future work, we wish to investigate how the improved data generation accuracy translates to model-based RL performance.

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RLJ Journal 2025 Journal Article

AVID: Adapting Video Diffusion Models to World Models

  • Marc Rigter
  • Tarun Gupta
  • Agrin Hilmkil
  • Chao Ma

Reinforcement learning (RL) is highly effective in domains that can be easily simulated. However, in problems such as robotic manipulation, accurate simulation is challenging and gathering large amounts of real-world data is impractical. A potential solution lies in leveraging widely-available unlabelled videos to train world models that simulate the consequences of actions. If the world model is accurate, it can be used to generate synthetic data to optimize decision-making via RL. Image-to-video diffusion models are already capable of generating highly realistic synthetic videos. However, these models are not action-conditioned, and the most powerful models are closed-source which means they cannot be finetuned. In this work, we propose to adapt pretrained video diffusion models to action-conditioned world models, without access to the parameters of the pretrained model. Our approach, AVID, trains an adapter on a small domain-specific dataset of action-labelled videos. AVID uses a learned mask to modify the intermediate outputs of the pretrained model and generate accurate action-conditioned videos. We evaluate AVID on video game and real-world robotics data, and show that it generally outperforms baselines for diffusion adaptation in video and image metrics. AVID demonstrates that pretrained video models have the potential to be powerful tools for generating synthetic data for RL agents. In future work, we wish to investigate how the improved data generation accuracy translates to model-based RL performance.

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ICML Conference 2024 Conference Paper

A Fixed-Point Approach for Causal Generative Modeling

  • Meyer Scetbon
  • Joel Jennings
  • Agrin Hilmkil
  • Cheng Zhang 0005
  • Chao Ma 0019

We propose a novel formalism for describing Structural Causal Models (SCMs) as fixed-point problems on causally ordered variables, eliminating the need for Directed Acyclic Graphs (DAGs), and establish the weakest known conditions for their unique recovery given the topological ordering (TO). Based on this, we design a two-stage causal generative model that first infers in a zero-shot manner a valid TO from observations, and then learns the generative SCM on the ordered variables. To infer TOs, we propose to amortize the learning of TOs on synthetically generated datasets by sequentially predicting the leaves of graphs seen during training. To learn SCMs, we design a transformer-based architecture that exploits a new attention mechanism enabling the modeling of causal structures, and show that this parameterization is consistent with our formalism. Finally, we conduct an extensive evaluation of each method individually, and show that when combined, our model outperforms various baselines on generated out-of-distribution problems.

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TMLR Journal 2024 Journal Article

Deep End-to-end Causal Inference

  • Tomas Geffner
  • Javier Antoran
  • Adam Foster
  • Wenbo Gong
  • Chao Ma
  • Emre Kiciman
  • Amit Sharma
  • Angus Lamb

Causal inference is essential for data-driven decision-making across domains such as business engagement, medical treatment, and policy making. However, in practice, causal inference suffers from many limitations including unknown causal graphs, missing data problems, and mixed data types. To tackle those challenges, we develop Deep End-to-end Causal Inference (DECI) framework, a flow based non-linear additive noise model combined with variational inference, which can perform both Bayesian causal discovery and inference. Theoretically, we show that DECI unifies many existing structural equation model (SEM) based causal inference techniques and can recover the ground truth mechanism under standard assumptions. Motivated by the challenges in the real world, we further extend DECI to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Empirically, we conduct extensive experiments (over a thousand) to show the competitive performance of DECI when compared to relevant baselines for both causal discovery and inference with both synthetic and causal machine learning benchmarks across data types and levels of missingness.

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ICML Conference 2024 Conference Paper

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention

  • Jiaqi Zhang
  • Joel Jennings
  • Agrin Hilmkil
  • Nick Pawlowski
  • Cheng Zhang 0005
  • Chao Ma 0019

Foundation models have brought changes to the landscape of machine learning, demonstrating sparks of human-level intelligence across a diverse array of tasks. However, a gap persists in complex tasks such as causal inference, primarily due to challenges associated with intricate reasoning steps and high numerical precision requirements. In this work, we take a first step towards building causally-aware foundation models for treatment effect estimations. We propose a novel, theoretically justified method called Causal Inference with Attention (CInA), which utilizes multiple unlabeled datasets to perform self-supervised causal learning, and subsequently enables zero-shot causal inference on unseen tasks with new data. This is based on our theoretical results that demonstrate the primal-dual connection between optimal covariate balancing and self-attention, facilitating zero-shot causal inference through the final layer of a trained transformer-type architecture. We demonstrate empirically that CInA effectively generalizes to out-of-distribution datasets and various real-world datasets, matching or even surpassing traditional per-dataset methodologies. These results provide compelling evidence that our method has the potential to serve as a stepping stone for the development of causal foundation models.

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ICLR Conference 2023 Conference Paper

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning

  • Matthew Ashman
  • Chao Ma 0019
  • Agrin Hilmkil
  • Joel Jennings
  • Cheng Zhang 0005

Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using ADMGs are based on either linear functional assumptions or a discrete search that is complicated to use and lacks computational tractability for large datasets. In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with nonlinear functional relations from observational data. We first show that the presence of latent confounding is identifiable under the assumptions of bow-free ADMGs with nonlinear additive noise models. With this insight, we propose a novel neural causal model based on autoregressive flows. This not only enables us to model complex causal relationships behind the data, but also estimate their functional relationships (hence treatment effects) simultaneously. We further validate our approach via experiments on both synthetic and real-world datasets, and demonstrate the competitive performance against relevant baselines.

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