553 research outputs found
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels
of exposure to a treatment is of high practical relevance for several important
fields, such as healthcare, economics and public policy. However, existing
methods for learning to estimate counterfactual outcomes from observational
data are either focused on estimating average dose-response curves, or limited
to settings with only two treatments that do not have an associated dosage
parameter. Here, we present a novel machine-learning approach towards learning
counterfactual representations for estimating individual dose-response curves
for any number of treatments with continuous dosage parameters with neural
networks. Building on the established potential outcomes framework, we
introduce performance metrics, model selection criteria, model architectures,
and open benchmarks for estimating individual dose-response curves. Our
experiments show that the methods developed in this work set a new
state-of-the-art in estimating individual dose-response
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal
effects on patient outcomes. Here, the gold standard for estimating causal
effects are randomized controlled trials; however, such trials are costly and
sometimes even unethical. Instead, medical practice is increasingly interested
in estimating causal effects among patient (sub)groups from electronic health
records, that is, observational data. In this paper, we aim at estimating the
average causal effect (ACE) from observational data (patient trajectories) that
are collected over time. For this, we propose DeepACE: an end-to-end deep
learning model. DeepACE leverages the iterative G-computation formula to adjust
for the bias induced by time-varying confounders. Moreover, we develop a novel
sequential targeting procedure which ensures that DeepACE has favorable
theoretical properties, i.e., is doubly robust and asymptotically efficient. To
the best of our knowledge, this is the first work that proposes an end-to-end
deep learning model tailored for estimating time-varying ACEs. We compare
DeepACE in an extensive number of experiments, confirming that it achieves
state-of-the-art performance. We further provide a case study for patients
suffering from low back pain to demonstrate that DeepACE generates important
and meaningful findings for clinical practice. Our work enables practitioners
to develop effective treatment recommendations based on population effects.Comment: Accepted at AAAI 202
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Unbiased CLTR requires click propensities to compensate for the difference
between user clicks and true relevance of search results via IPS. Current
propensity estimation methods assume that user click behavior follows the PBM
and estimate click propensities based on this assumption. However, in reality,
user clicks often follow the CM, where users scan search results from top to
bottom and where each next click depends on the previous one. In this cascade
scenario, PBM-based estimates of propensities are not accurate, which, in turn,
hurts CLTR performance. In this paper, we propose a propensity estimation
method for the cascade scenario, called CM-IPS. We show that CM-IPS keeps CLTR
performance close to the full-information performance in case the user clicks
follow the CM, while PBM-based CLTR has a significant gap towards the
full-information. The opposite is true if the user clicks follow PBM instead of
the CM. Finally, we suggest a way to select between CM- and PBM-based
propensity estimation methods based on historical user clicks.Comment: 4 pages, 2 figures, 43rd International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR '20
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