5 research outputs found
Estimating Treatment Effects using Neurosymbolic Program Synthesis
Estimating treatment effects from observational data is a central problem in
causal inference. Methods to solve this problem exploit inductive biases and
heuristics from causal inference to design multi-head neural network
architectures and regularizers. In this work, we propose to use neurosymbolic
program synthesis, a data-efficient, and interpretable technique, to solve the
treatment effect estimation problem. We theoretically show that neurosymbolic
programming can solve the treatment effect estimation problem. By designing a
Domain Specific Language (DSL) for treatment effect estimation problem based on
the inductive biases used in literature, we argue that neurosymbolic
programming is a better alternative to treatment effect estimation than
traditional methods. Our empirical study reveals that our method, which
implicitly encodes inductive biases in a DSL, achieves better performance on
benchmark datasets than the state-of-the-art methods.Comment: Preprin
Counterfactual Generation Under Confounding
A machine learning model, under the influence of observed or unobserved
confounders in the training data, can learn spurious correlations and fail to
generalize when deployed. For image classifiers, augmenting a training dataset
using counterfactual examples has been empirically shown to break spurious
correlations. However, the counterfactual generation task itself becomes more
difficult as the level of confounding increases. Existing methods for
counterfactual generation under confounding consider a fixed set of
interventions (e.g., texture, rotation) and are not flexible enough to capture
diverse data-generating processes. Given a causal generative process, we
formally characterize the adverse effects of confounding on any downstream
tasks and show that the correlation between generative factors (attributes) can
be used to quantitatively measure confounding between generative factors. To
minimize such correlation, we propose a counterfactual generation method that
learns to modify the value of any attribute in an image and generate new images
given a set of observed attributes, even when the dataset is highly confounded.
These counterfactual images are then used to regularize the downstream
classifier such that the learned representations are the same across various
generative factors conditioned on the class label. Our method is
computationally efficient, simple to implement, and works well for any number
of generative factors and confounding variables. Our experimental results on
both synthetic (MNIST variants) and real-world (CelebA) datasets show the
usefulness of our approach
Causal Inference Using LLM-Guided Discovery
At the core of causal inference lies the challenge of determining reliable
causal graphs solely based on observational data. Since the well-known backdoor
criterion depends on the graph, any errors in the graph can propagate
downstream to effect inference. In this work, we initially show that complete
graph information is not necessary for causal effect inference; the topological
order over graph variables (causal order) alone suffices. Further, given a node
pair, causal order is easier to elicit from domain experts compared to graph
edges since determining the existence of an edge can depend extensively on
other variables. Interestingly, we find that the same principle holds for Large
Language Models (LLMs) such as GPT-3.5-turbo and GPT-4, motivating an automated
method to obtain causal order (and hence causal effect) with LLMs acting as
virtual domain experts. To this end, we employ different prompting strategies
and contextual cues to propose a robust technique of obtaining causal order
from LLMs. Acknowledging LLMs' limitations, we also study possible techniques
to integrate LLMs with established causal discovery algorithms, including
constraint-based and score-based methods, to enhance their performance.
Extensive experiments demonstrate that our approach significantly improves
causal ordering accuracy as compared to discovery algorithms, highlighting the
potential of LLMs to enhance causal inference across diverse fields
Rethinking Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mitigate
confounding biases in the training data for a machine learning model. These
biases, such as spurious correlations, arise due to various observed and
unobserved confounding variables in the data generation process. In this paper,
we formally analyze how confounding biases impact downstream classifiers and
present a causal viewpoint to the solutions based on counterfactual data
augmentation. We explore how removing confounding biases serves as a means to
learn invariant features, ultimately aiding in generalization beyond the
observed data distribution. Additionally, we present a straightforward yet
powerful algorithm for generating counterfactual images, which effectively
mitigates the influence of confounding effects on downstream classifiers.
Through experiments on MNIST variants and the CelebA datasets, we demonstrate
the effectiveness and practicality of our approach
On Causally Disentangled Representations
Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. Initially consisting of unsupervised models with independence assumptions, more recently, weak supervision and correlated features have been explored, but without a causal view of the generative process. In contrast, we work under the regime of a causal generative process where generative factors are either independent or can be potentially confounded by a set of observed or unobserved confounders. We present an analysis of disentangled representations through the notion of disentangled causal process. We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. We show that our metrics capture the desiderata of disentangled causal process. Finally we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective