Portrait of Dhanya Sridhar

Dhanya Sridhar

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Causality
Deep Learning
Probabilistic Models
Reasoning
Representation Learning

Biography

Dhanya Sridhar is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

She was a postdoctoral researcher at Columbia University and received her doctorate from the University of California, Santa Cruz.

In brief, Sridhar’s research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently and discover new knowledge alongside us.

Current Students

Collaborating researcher - Helmholtz AI
Independent visiting researcher - University of Maryland College Park
Master's Research - Université de Montréal
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PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
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PhD - Université de Montréal
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Publications

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad Paul Kording
Karen Sachs
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad P. Kording
Karen Sachs
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad P. Kording
Karen Sachs
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (see more)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
Given two sets of elements (such as cell types and drug compounds), researchers typically only have access to a limited subset of their inte… (see more)ractions. The task of causal imputation involves using this subset to predict unobserved interactions. Squires et al. (2022) have proposed two estimators for this task based on the synthetic interventions (SI) estimator: SI-A (for actions) and SI-C (for contexts). We extend their work and introduce a novel causal imputation estimator, generalized synthetic interventions (GSI). We prove the identifiability of this estimator for data generated from a more complex latent factor model. On synthetic and real data we show empirically that it recovers or outperforms their estimators.
General Causal Imputation via Synthetic Interventions
Marco Jiralerspong
Thomas Jiralerspong
Vedant Shah
In-context learning and Occam's razor
Eric Elmoznino
Tom Marty
Tejas Kasetty
Leo Gagnon
Sarthak Mittal
Mahan Fathi
A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees fo… (see more)r generalization without further assumptions, in practice we observe that simple models which explain the training data generalize best: a principle called Occam's razor. Despite the need for simple models, most current approaches in machine learning only minimize the training error, and at best indirectly promote simplicity through regularization or architecture design. Here, we draw a connection between Occam's razor and in-context learning: an emergent ability of certain sequence models like Transformers to learn at inference time from past observations in a sequence. In particular, we show that the next-token prediction loss used to train in-context learners is directly equivalent to a data compression technique called prequential coding, and that minimizing this loss amounts to jointly minimizing both the training error and the complexity of the model that was implicitly learned from context. Our theory and the empirical experiments we use to support it not only provide a normative account of in-context learning, but also elucidate the shortcomings of current in-context learning methods, suggesting ways in which they can be improved. We make our code available at https://github.com/3rdCore/PrequentialCode.
Evaluating Interventional Reasoning Capabilities of Large Language Models
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (see more)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts.
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard
Sébastien Lachapelle
Julia Kaltenborn
Yaniv Gurwicz
Peer Nowack
Jakob Runge
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scienti… (see more)sts study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\~no phenomenon and other processes, as well as the causal model over them. The challenge is that this task, called causal representation learning, is highly underdetermined from observational data alone, requiring other constraints during learning to resolve the indeterminacies. In this work, we consider a temporal model with a sparsity assumption, namely single-parent decoding: each observed low-level variable is only affected by a single latent variable. Such an assumption is reasonable in many scientific applications that require finding groups of low-level variables, such as extracting regions from geographically gridded measurement data in climate research or capturing brain regions from neural activity data. We demonstrate the identifiability of the resulting model and propose a differentiable method, Causal Discovery with Single-parent Decoding (CDSD), that simultaneously learns the underlying latents and a causal graph over them. We assess the validity of our theoretical results using simulated data and showcase the practical validity of our method in an application to real-world data from the climate science field.
In-Context Learning, Can It Break Safety?
Sophie Xhonneux
David Dobre
Michael Noukhovitch
Demystifying amortized causal discovery with transformers
Francesco Montagna
Max Cairney-Leeming
Francesco Locatello
Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding… (see more) explicit assumptions that traditional methods make for identifiability. In this work, we investigate CSIvA \citep{ke2023learning}, a transformer-based model promising to train on synthetic data and transfer to real data. First, we bridge the gap with existing identifiability theory and show that constraints on the training data distribution implicitly define a prior on the test observations. Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable. At the same time, we find new trade-offs. Training on datasets generated from different classes of causal models, unambiguously identifiable in isolation, improves the test generalization. Performance is still guaranteed, as the ambiguous cases resulting from the mixture of identifiable causal models are unlikely to occur (which we formally prove). Overall, our study finds that amortized causal discovery still needs to obey identifiability theory, but it also differs from classical methods in how the assumptions are formulated, trading more reliance on assumptions on the noise type for fewer hypotheses on the mechanisms.
Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne
Jason Hartford