Searching For Causal Genetic Mechanisms Across Human Populations
Chiara Sabatti (Stanford University)
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Learning from RCTs in Public Health and Medicine
James Robins (Harvard University)Cooling quantum systems with quantum information processing
Nayeli Azucena Rodríguez Briones Technische Universität Wien
Panel Discussion
Elias Bareinboim (Columbia University), Frederick Eberhardt (Caltech), Kun Zhang (Carnegie Mellon University), and Uri Shalit (Technion - Israel Institute of Technology)Online Reinforcement Learning With The Help Of Confounded Offline Data
Uri Shalit (Technion - Israel Institute of Technology)Learning & Reasoning With Soft Interventions
Elias Bareinboim (Columbia University)Learning Causal Representations From Unknown Interventions
Kun Zhang (Carnegie Mellon University)Causal Emergence: When Distortions In A Map Obscure The Territory
Frederick Eberhardt (Caltech)Measurement-induced criticality and charge-sharpening transitions
Romain Vasseur University of Massachusetts Amherst
Searching For Causal Genetic Mechanisms Across Human Populations
Chiara Sabatti (Stanford University)Identifying which genetic variants influence medically relevant phenotypes is an important task both for therapeutic development and for risk prediction. In the last decade, genome wide association studies have been the most widely-used instrument to tackle this question. One challenge that they encounter is in the interplay between genetic variability and the structure of human populations. In this talk, we will focus on some opportunities that arise when one collects data from diverse populations and present statistical methods that allow us to leverage them. The presentation will be based on joint work with M. Sesia, S. Li, Z. Ren, Y. Romano and E. Candes.To Adjust Or Not To Adjust? Estimating The Average Treatment Effect In Randomized Experiments With Missing Covariates
Peng Ding (UC Berkeley)Complete randomization allows for consistent estimation of the average treatment effect based on the difference in means of the outcomes without strong modeling assumptions on the outcome-generating process. Appropriate use of the pretreatment covariates can further improve the estimation efficiency. However, missingness in covariates is common in experiments and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The unadjusted difference in means is always unbiased. The complete-covariate analysis adjusts for all completely observed covariates and improves the efficiency of the difference in means if at least one completely observed covariate is predictive of the outcome. Then what is the additional gain of adjusting for covariates subject to missingness? A key insight is that the missingness indicators act as fully observed pretreatment covariates as long as missingness is not affected by the treatment, and can thus be used in covariate adjustment to bring additional estimation efficiency. This motivates adding the missingness indicators to the regression adjustment, yielding the missingness-indicator method as a well-known but not so popular strategy in the literature of missing data. We recommend it due to its many advantages. We also propose modifications to the missingness-indicator method based on asymptotic and finite-sample considerations. To reconcile the conflicting recommendations in the missing data literature, we analyze and compare various strategies for analyzing randomized experiments with missing covariates under the design-based framework. This framework treats randomization as the basis for inference and does not impose any modeling assumptions on the outcome-generating process and missing-data mechanism.Learning from RCTs in Public Health and Medicine
James Robins (Harvard University)RCTs are potentially useful in many ways other than standard confirmatory intent to treat (ITT) analyses, but to succeed difficult problems must be overcome.I will discuss some or (time-permitting) all of the following problems : 1. The problem of transportability of the trial results to other populations: I will explain why transportability is much more difficult in trials comparing longitudinal dynamic treatment regimes rather than in simple point treatment trials. 2. The problematic use of RCT data in micro-simulation models used in cost-benefit analyses 3.The problem of combining data from large, often confounded, administrative or electronic medical records , with data from smaller underpowered randomized trials in estimating individualized treatment strategies. 4. The problem of using the results of RCTs to benchmark the ability of observational analyses to 'get it right', with the goal of providing evidence that causal analyses of observational data are sufficiently reliable to contribute to decision making 5.The problem noncompliance with assigned protocol in trials in which the per-protocol effect rather than the ITT effect is of substantive importance . 6. The problem of leveraging the prior knowledge that diagnostic tests have "no direct effect on the outcome except through the treatment delivered" to greatly increase the power of trials designed to estimate the cost vs benefit of competing testing strategies.Counterfactual Inference For Sequential Experiment Design
Raaz Dwivedi (MIT)We consider the problem of counterfactual inference in sequentially designed experiments wherein a collection of units undergo a sequence of interventions based on policies adaptive over time, and outcomes are observed based on the assigned interventions. Our goal is counterfactual inference, i.e., estimate what would have happened if alternate policies were used, a problem that is inherently challenging due to the heterogeneity in the outcomes across users and time. In this work, we identify structural assumptions that allow us to impute the missing potential outcomes in sequential experiments, where the policy is allowed to adapt simultaneously to all users' past data. We prove that under suitable assumptions on the latent factors and temporal dynamics, a variant of the nearest neighbor strategy allows us to impute the missing information using the observed outcome across time and users. Under mild assumptions on the adaptive policy and the underlying latent factor model, we prove that using data till time t for N users in the study, our estimate for the missing potential outcome at time t+1 admits a mean squared-error that scales as t^{-1/2+\delta} + N^{-1+\delta} for any \delta>0, for any fixed user. We also provide an asymptotic confidence interval for each outcome under suitable growth conditions on N and t, which can then be used to build confidence intervals for individual treatment effects. Our work extends the recent literature on inference with adaptively collected data by allowing for policies that pool across users, the matrix completion literature for missing at random settings by allowing for adaptive sampling mechanisms, and missing data problems in multivariate time series by allowing for a generic non-parametric model.Cooling quantum systems with quantum information processing
Nayeli Azucena Rodríguez Briones Technische Universität Wien
The field of quantum information provides fundamental insight into central open questions in quantum thermodynamics and quantum many-body physics, such as the characterization of the influence of quantum effects on the flow of energy and information. These insights have inspired new methods for cooling physical systems at the quantum scale using tools from quantum information processing. These protocols not only provide an essentially different way to cool, but also go beyond conventional cooling techniques, bringing important applications for quantum technologies. In this talk, I will first review the basic ideas of algorithmic cooling and give analytical results for the achievable cooling limits for the conventional heat-bath version. Then, I will show how the limits can be circumvented by using quantum correlations. In one algorithm I take advantage of correlations that can be created during the rethermalization step with the heat-bath and in another I use correlations present in the initial state induced by the internal interactions of the system. Finally, I will present a recently fully characterized quantum property of quantum many-body systems, in which entanglement in low-energy eigenstates can obstruct local outgoing energy flows.
Panel Discussion
Elias Bareinboim (Columbia University), Frederick Eberhardt (Caltech), Kun Zhang (Carnegie Mellon University), and Uri Shalit (Technion - Israel Institute of Technology)Online Reinforcement Learning With The Help Of Confounded Offline Data
Uri Shalit (Technion - Israel Institute of Technology)I will present recent work exploring how and when can confounded offline data be used to improve online reinforcement learning. We will explore conditions of partial observability and distribution shifts between the offline and online environments, and present results for contextual bandits, imitation learning and reinforcement learning.Learning & Reasoning With Soft Interventions
Elias Bareinboim (Columbia University)In this talk, I will discuss recent work on reasoning and learning with soft interventions, including the problem of identification, extrapolation/transportability, and structural learning. I will also briefly discuss a new calculus, which generalizes the do-calculus, as well as algorithmic and graphical conditions. Supporting material: General Transportability of Soft Interventions: Completeness Results . J. Correa, E. Bareinboim. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020. https://causalai.net/r68.pdf Causal Discovery from Soft Interventions with Unknown Targets: Characterization & Learning. A. Jaber, M. Kocaoglu, K. Shanmugam, E. Bareinboim. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020. https://causalai.net/r67.pdf A Calculus For Stochastic Interventions: Causal Effect Identification and Surrogate Experiments J. Correa, E. Bareinboim. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), 2019. https://causalai.net/r55.pdfLearning Causal Representations From Unknown Interventions
Kun Zhang (Carnegie Mellon University)Learning causal representations from observational data can be viewed as a task of identifying where and how the interventions were applied--this reveals information of the causal representations at the same time. Given that this learning task is a typical inverse problem, an essential issue is the establishment of identifiability results: one has to guarantee that the learned representations are consistent with the underlying causal process. Dealing with this issue generally involves appropriate assumptions. In this talk, I focus on learning latent causal variables and their causal relations, together with their relations with the measured variables, from observational data. I show what assumptions, together with instantiations of the "minimal change" principle, render the underlying causal representations identifiable across several settings. Specifically, in the i.i.d. case, the identifiability benefits from appropriate parametric assumptions on the causal relations and a certain type of "minimality" assumption. Temporal dependence makes it possible to recover latent temporally causal processes from time series data without parametric assumptions, and nonstationarity further improves the identifiability. I then draw the connection between recent advances in nonlinear independent component analysis and the minimal change principle. Finally, concerning the nonparametric setting with changing instantaneous causal relations, I show how to learn the latent variables with changing causal relations in light of the minimal change principle.Causal Emergence: When Distortions In A Map Obscure The Territory
Frederick Eberhardt (Caltech)We provide a critical assessment of the account of causal emergence presented in Hoel (2017). The account integrates causal and information theoretic concepts to explain under what circumstances there can be causal descriptions of a system at multiple scales of analysis. We show that the causal macro variables implied by this account result in interventions with significant ambiguity, and that the operations of marginalization and abstraction do not commute. Both of these are desiderata that, we argue, any account of multi-scale causal analysis should be sensitive to. The problems we highlight in Hoel's definition of causal emergence derive from the use of various averaging steps and the introduction of a maximum entropy distribution that is extraneous to the system under investigation. (This is joint work with Lin Lin Lee.)Measurement-induced criticality and charge-sharpening transitions
Romain Vasseur University of Massachusetts Amherst
Monitored quantum circuits (MRCs) exhibit a measurement-induced phase transition between area-law and volume-law entanglement scaling. In this talk, I will argue that MRCs with a conserved charge additionally exhibit two distinct volume-law entangled phases that cannot be characterized by equilibrium notions of symmetry-breaking or topological order, but rather by the non-equilibrium dynamics and steady-state distribution of charge fluctuations. These include a charge-fuzzy phase in which charge information is rapidly scrambled leading to slowly decaying spatial fluctuations of charge in the steady state, and a charge-sharp phase in which measurements collapse quantum fluctuations of charge without destroying the volume-law entanglement of neutral degrees of freedom. I will present some statistical mechanics and effective field theory approaches to such charge-sharpening transitions.
Zoom Link: https://pitp.zoom.us/meeting/register/tJcqc-ihqzMvHdW-YBm7mYd_XP9Amhypv5vO