461 research outputs found
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Counterfactual inference for continuous rather than binary treatment
variables is more common in real-world causal inference tasks. While there are
already some sample reweighting methods based on Marginal Structural Model for
eliminating the confounding bias, they generally focus on removing the
treatment's linear dependence on confounders and rely on the accuracy of the
assumed parametric models, which are usually unverifiable. In this paper, we
propose a de-confounding representation learning (DRL) framework for
counterfactual outcome estimation of continuous treatment by generating the
representations of covariates disentangled with the treatment variables. The
DRL is a non-parametric model that eliminates both linear and nonlinear
dependence between treatment and covariates. Specifically, we train the
correlations between the de-confounded representations and the treatment
variables against the correlations between the covariate representations and
the treatment variables to eliminate confounding bias. Further, a
counterfactual inference network is embedded into the framework to make the
learned representations serve both de-confounding and trusted inference.
Extensive experiments on synthetic datasets show that the DRL model performs
superiorly in learning de-confounding representations and outperforms
state-of-the-art counterfactual inference models for continuous treatment
variables. In addition, we apply the DRL model to a real-world medical dataset
MIMIC and demonstrate a detailed causal relationship between red cell width
distribution and mortality.Comment: 15 pages,4 figure
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
Causally Disentangled Generative Variational AutoEncoder
We present a new supervised learning technique for the Variational
AutoEncoder (VAE) that allows it to learn a causally disentangled
representation and generate causally disentangled outcomes simultaneously. We
call this approach Causally Disentangled Generation (CDG). CDG is a generative
model that accurately decodes an output based on a causally disentangled
representation. Our research demonstrates that adding supervised regularization
to the encoder alone is insufficient for achieving a generative model with CDG,
even for a simple task. Therefore, we explore the necessary and sufficient
conditions for achieving CDG within a specific model. Additionally, we
introduce a universal metric for evaluating the causal disentanglement of a
generative model. Empirical results from both image and tabular datasets
support our findings
Adversarial De-confounding in Individualised Treatment Effects Estimation
Observational studies have recently received significant attention from the
machine learning community due to the increasingly available non-experimental
observational data and the limitations of the experimental studies, such as
considerable cost, impracticality, small and less representative sample sizes,
etc. In observational studies, de-confounding is a fundamental problem of
individualised treatment effects (ITE) estimation. This paper proposes
disentangled representations with adversarial training to selectively balance
the confounders in the binary treatment setting for the ITE estimation. The
adversarial training of treatment policy selectively encourages
treatment-agnostic balanced representations for the confounders and helps to
estimate the ITE in the observational studies via counterfactual inference.
Empirical results on synthetic and real-world datasets, with varying degrees of
confounding, prove that our proposed approach improves the state-of-the-art
methods in achieving lower error in the ITE estimation.Comment: accepted to AISTATS 202
Measuring axiomatic soundness of counterfactual image models
We use the axiomatic definition of counterfactual to derive metrics that enable quantifying the correctness of approximate counterfactual inference models. Abstract: We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case. Motivated by these issues, we revisit Pearl's axiomatic definition of counterfactuals to determine the necessary constraints of any counterfactual inference model: composition, reversibility, and effectiveness. We frame counterfactuals as functions of an input variable, its parents, and counterfactual parents and use the axiomatic constraints to restrict the set of functions that could represent the counterfactual, thus deriving distance metrics between the approximate and ideal functions. We demonstrate how these metrics can be used to compare and choose between different approximate counterfactual inference models and to provide insight into a model's shortcomings and trade-offs
Decomposing Counterfactual Explanations for Consequential Decision Making
The goal of algorithmic recourse is to reverse unfavorable decisions (e.g.,
from loan denial to approval) under automated decision making by suggesting
actionable feature changes (e.g., reduce the number of credit cards). To
generate low-cost recourse the majority of methods work under the assumption
that the features are independently manipulable (IMF). To address the feature
dependency issue the recourse problem is usually studied through the causal
recourse paradigm. However, it is well known that strong assumptions, as
encoded in causal models and structural equations, hinder the applicability of
these methods in complex domains where causal dependency structures are
ambiguous. In this work, we develop \texttt{DEAR} (DisEntangling Algorithmic
Recourse), a novel and practical recourse framework that bridges the gap
between the IMF and the strong causal assumptions. \texttt{DEAR} generates
recourses by disentangling the latent representation of co-varying features
from a subset of promising recourse features to capture the main practical
recourse desiderata. Our experiments on real-world data corroborate our
theoretically motivated recourse model and highlight our framework's ability to
provide reliable, low-cost recourse in the presence of feature dependencies
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