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

DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction

Leveraging labelled data from multiple domains to enable prediction in another domain without labels is a significant, yet challenging problem. To address this problem, we introduce the framework DAPDAG (\textbf{D}omain \textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and propose to learn an auto-encoder that undertakes inference on population statistics given features and reconstructing a directed acyclic graph (DAG) as an auxiliary task. The underlying DAG structure is assumed invariant among observed variables whose conditional distributions are allowed to vary across domains led by a latent environmental variable $E$. The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$. We train the encoder and decoder jointly in an end-to-end manner and conduct experiments on synthetic and real datasets with mixed variables. Empirical results demonstrate that reconstructing the DAG benefits the approximate inference. Furthermore, our approach can achieve competitive performance against other benchmarks in prediction tasks, with better adaptation ability, especially in the target domain significantly different from the source domains

Personalized Medicine using Real-World Data: a Causal Machine Learning Approach

Personalized medicine has the potential to revolutionize how healthcare is provided. The aim of personalized medicine is to tailor treatment decisions to the individual patient. To this end, we must estimate what effect a particular treatment has on the individual patient. However, estimating this individualized treatment effect is a challenging undertaking, since conventional medicine uses clinical trials to estimate treatment effects. These clinical trials are usually too small to capture all possible variations of patients. As such, clinical trials can only provide treatment effects for the average patient, but not for the individual patient. In order to estimate individualized treatment effects, so-called real-world data (RWD) bear great potential. Since RWD are generated outside of clinical trials, they are available in large quantities and, hence, can capture more variations among patients. However, it is precisely because RWD are generated outside of clinical trials that standard analytics tools may yield incorrect estimates of treatment effects. This is because treatment effects are causal effects and, hence, analytics tools for RWD must be able distinguish causality from correlation. Therefore, leveraging RWD for personalized medicine requires the development of advanced analytics tools that can reliably estimate individualized treatment effects. In this thesis, we approach personalized medicine by leveraging RWD. For this, we develop novel methods in the field of causal machine learning. These methods are able to estimate causal effects and, hence, can be used to reliably estimate treatment effects from RWD. In the first three chapters of this thesis, we develop causal machine learning methods that leverage RWD in order to: (I) estimate average treatment effects with high accuracy, (ii) learn personalized treatment policies that can be robustly generalized to the entire population, and (iii) estimate individualized treatment effects. While we show the potential of causal machine learning for leveraging RWD, we also demonstrate the drawbacks, which occur, since RWD are not generated in clinical trials. As a remedy, in the fourth chapter of this thesis, (iv) we adopt a complementary view of data from clinical trials and RWD and propose to combine both types of data for estimating individualized treatment effects. This allows to cope with the drawbacks of RWD, while fully leveraging its potential for personalized medicine

Über Zentralisatoralgebren von Tensorprodukten irreduzibler Weylmoduln

Tensor products of natural modules and their centralizers are well-studied in the literature. This thesis extends classical results to tensor products of irreducible Weyl modules over a quantum group. We show that their centralizer algebras are cellular in the generic case, as pointed out in the literature, as well as under specializations. Their cellular basis, the dual canonical centralizer basis, is constructed directly from Lusztig's canonical basis of the tensor space and is given explicitly. It turns out that the cells can be derived directly from the Weyl filtration. We also describe the projective and simple modules of these centralizer algebras and describe the tensor space as module of the centralizer algebra. It is furthermore shown that the decomposition matrix of cell modules can be described in terms of Weyl filtrations of indecomposable tilting modules. The results obtained cover classical results where, for example, the centralizer is a quotient of the Hecke algebra. The latter has the Kazhdan-Lusztig basis as cellular basis. It turns out that the dual canonical centralizer basis coincides up to lower cell ideals with the Kazdan-Lusztig basis. Therefore, the dual canonical centralizer basis is a generalization of the Kazhdan-Lusztig basis to a much wider class of algebras.Tensorprodukte natürlicher Moduln und ihre Zentralisatoren sind in der Literatur gut untersucht. Diese Arbeit erweitert klassische Ergebnisse auf Tensorprodukte irreduzibler Weylmoduln für eine Quantengruppe. Es wird gezeigt, dass diese Zentralisatoralgebren zellulär sind sowohl im generischen Fall, wie bereits in der Literatur angemerkt, als auch unter Spezialisierung. Ihre zelluläre Basis, die duale kanonische Zentralisatorbasis, wird direkt aus Lusztigs kanonischer Basis auf dem Tensorraum konstruiert und explizit angegeben. Es werden die projektiven und einfachen Moduln dieser Zentralisatoralgebren beschrieben ebenso wie die Struktur des Tensorraums als Modul des Zentralisators. Es wird weiterhin gezeigt, dass die Zerlegungsmatrix der Zellmoduln duch die Weylfiltrierung unzerlegbarer Kippmoduln beschrieben werden kann. Die erzielten Ergebnisse decken auch klassische Fälle ab, wo z.B. der Zentralisator Quotient der Hecke Algebra ist. Letztere hat u.a. die Kazhdan-Lusztig-Basis als zelluläre Basis. Es stellt sich heraus, dass die duale kanonische Zentralisatorbasis bis auf tieferliegende Zellideale mit den Kazhdan-Lusztig-Basis übereinstimmt. Damit ist die duale kanonische Zentralisatorbasis eine Verallgemeinerung der Kazhdan-Lusztig-Basis auf eine wesentlich größere Klasse von Zentralisatoralgebren

Interpretable Off-Policy Learning via Hyperbox Search

Personalized treatment decisions have become an integral part of modern medicine. Thereby, the aim is to make treatment decisions based on individual patient characteristics. Numerous methods have been developed for learning such policies from observational data that achieve the best outcome across a certain policy class. Yet these methods are rarely interpretable. However, interpretability is often a prerequisite for policy learning in clinical practice. In this paper, we propose an algorithm for interpretable off-policy learning via hyperbox search. In particular, our policies can be represented in disjunctive normal form (i.e., OR-of-ANDs) and are thus intelligible. We prove a universal approximation theorem that shows that our policy class is flexible enough to approximate any measurable function arbitrarily well. For optimization, we develop a tailored column generation procedure within a branch-and-bound framework. Using a simulation study, we demonstrate that our algorithm outperforms state-of-the-art methods from interpretable off-policy learning in terms of regret. Using real-word clinical data, we perform a user study with actual clinical experts, who rate our policies as highly interpretable.ISSN:2640-349

Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all confounders are observed. However, in practice, it is unlikely that we observe these confounders directly. Instead, we often observe noisy measurements of true confounders, which can serve as valid proxies. In this paper, we address the problem of estimating ITE in the longitudinal setting where we observe noisy proxies instead of true confounders. To this end, we develop the Deconfounding Temporal Autoencoder (DTA), a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders. In particular, the DTA combines a long short-term memory autoencoder with a causal regularization penalty that renders the potential outcomes and treatment assignment conditionally independent given the learned hidden embedding. Once the hidden embedding is learned via DTA, state-of-the-art outcome models can be used to control for it and obtain unbiased estimates of ITE. Using synthetic and real-world medical data, we demonstrate the effectiveness of our DTA by improving over state-of-the-art benchmarks by a substantial margin.ISSN:2640-349

Estimating Conditional Average Treatment Effects with Missing Treatment Information

Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where, thus, unique challenges arise in the form of covariate shifts. We identify two covariate shifts in our setting: (i) a covariate shift between the treated and control population; and (ii) a covariate shift between the observed and missing treatment population. We first theoretically show the effect of these covariate shifts by deriving a generalization bound for estimating CATE in our setting with missing treatments. Then, motivated by our bound, we develop the missing treatment representation network (MTRNet), a novel CATE estimation algorithm that learns a balanced representation of covariates using domain adaptation. By using balanced representations, MTRNet provides more reliable CATE estimates in the covariate domains where the data are not fully observed. In various experiments with semi-synthetic and real-world data, we show that our algorithm improves over the state-of-the-art by a substantial margin

Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all confounders are observed. However, in practice, it is unlikely that we observe these confounders directly. Instead, we often observe noisy measurements of true confounders, which can serve as valid proxies. In this paper, we address the problem of estimating ITE in the longitudinal setting where we observe noisy proxies instead of true confounders. To this end, we develop the Deconfounding Temporal Autoencoder (DTA), a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders. In particular, the DTA combines a long short-term memory autoencoder with a causal regularization penalty that renders the potential outcomes and treatment assignment conditionally independent given the learned hidden embedding. Once the hidden embedding is learned via DTA, state-of-the-art outcome models can be used to control for it and obtain unbiased estimates of ITE. Using synthetic and real-world medical data, we demonstrate the effectiveness of our DTA by improving over state-of-the-art benchmarks by a substantial margin.ISSN:2640-349

AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units

Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs. Specifically, we develop an attentive deep Markov model called AttDMM. To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different disease states in health trajectory (via a latent variable model). Our evaluations were based on an established baseline dataset (MIMIC-III) with 53,423 ICU stays. The results confirm that compared to state-of-the-art baselines, our AttDMM was superior: AttDMM achieved an area under the receiver operating characteristic curve (AUROC) of 0.876, which yielded an improvement over the state-of-the-art method by 2.2%. In addition, the risk score from the AttDMM provided warnings several hours earlier. Thereby, our model shows a path towards identifying patients at risk so that health practitioners can intervene early and save patient lives