(The talk will be given in English)

Speaker:     Prof. Daniel Nevo

                          Department of Statistics and Operations Research, Tel Aviv University 

Monday, August 5^th, 2024

12:00 - 13:00

Causal inference with misspecified network interference

Abstract

The typical approach towards drawing causal conclusions from observed data starts by defining a causal estimand, for example in terms of potential outcomes or the so-called do operator, and continues by providing conditions for identification of this estimand from the data, followed by statistical estimation and inference. One of the main assumptions is the no-interference assumption, meaning that the treatment assigned to one unit does not affect other units in the sample. However, in many domains such as in the social sciences and infectious disease epidemiology, this assumption is implausible in practice due to social interactions. 

 As an alternative to the no-interference assumption, an interference structure is often represented using a network. Ubiquitously, the network structure is assumed to be known and correctly specified. Nevertheless, correctly encoding the interference structure in a network can be challenging. For example, people may misreport their social connections, or report connections irrelevant to the specific combination of treatment and outcome.

Building on the exposure mapping framework, we derive the bias arising from estimating causal effects under a misspecified interference structure. To address this problem, we propose a novel estimator that uses multiple networks simultaneously and is unbiased if one of the networks correctly represents the interference structure, thus providing robustness to the network specification. Additionally, we propose a sensitivity analysis that quantifies the impact of a postulated misspecification mechanism on the causal estimates. Through simulation studies, we illustrate the bias from assuming an incorrect network and show the bias-variance tradeoff of our proposed network-misspecification-robust estimator. We further demonstrate the utility of our methods in two real examples.

Joint work with Bar Weinstein

Short Bio

Daniel Nevo is an Associate Professor at the Department of Statistics and Operations Research in Tel Aviv University. He received PhD in Statistics in 2016 from The Hebrew University, working on high-dimensional regression problems and biostatistical methods. Then he was a postdoc at Harvard Departments of Biostatistics and Epidemiology, before joining TAU in 2018. Daniel's research focuses on developing and implementing statistical methods in widespread domains, with emphasis on theory and methods for causal inference.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בטופס הנוכחות שיועבר באולם במהלך הסמינר

School of Mechanical Engineering Seminar
Wednesday, August 7, 2024 at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Learning In-Hand Perception and Manipulation with Adaptive Robotic Hands

Osher Azulay

Advisor: Dr. Avishai Sintov

Robots require efficient interaction with a large variety of objects in different environments, from industrial applications to domestic tasks. The interaction involves grasping and dexterous manipulation of objects to complete various tasks. Whereas the ability to manipulate an object within the hand is a fundamental task for humans, it remains challenging for robots. To cope with the diversity of objects and hand manipulation tasks in the real world, robots need to understand their environment and quickly infer the physical properties of objects.

Despite the impressive accuracy of industrial robotic hands, their complexity, fragility, high cost, and control challenges remain significant obstacles. Yet, the development of affordable, robust, and adaptable robot hardware has created opportunities to dramatically enhance robot autonomy. However, a major challenge for this new hardware is coping with the constant variability and uncertainty of real-world environments. Recently, underactuated hands, which are simpler and more flexible, have emerged as a promising alternative. Nevertheless, they present modeling difficulties due to their inherent uncertainties.

Our current focus is on integrating these advancements into learnable tactile-based policies to enable robots to better understand external contacts and adapt to different  tasks. By tightly coupling perception and action, we wish to allow the hand, despite its compliant nature, to perform complex tasks through self-supervision with minimal human effort and rapid adaptation to new situations. This research lays the algorithmic foundation for robot sensing, applicable to various robotic systems to overcome uncertainties, making high-capability robots more accessible.

Electrical Engineering Systems Seminar

Speaker: Shahar Stein Ioushua

Ph.D. student under the supervision of Prof. Ofer Shayevitz

Sunday, 4^th August 2024, at 15:00

Room 011, Kitot Building, Faculty of Engineering

Problems in Information Theory and High Dimensional Statistics

Abstract

We study problems in information theory and statistics where high-dimension plays a key role. Often in such problems, the near-asymptotic setting enables the use of mathematical tools that are not applicable in low-dimension.

First, we study the problem of counting the number of graphs with a given subgraph distribution.

Le G be a large (simple, unlabeled) dense graph on n vertices. Suppose that we only know, or can estimate, the empirical distribution of the number of subgraphs F that each vertex in G participates in, for some fixed small graph F. How many other graphs would look essentially the same to us, i.e., would have a similar local structure? We derive upper and lower bounds on the number of graphs whose empirical distribution lies close (in the Kolmogorov-Smirnov distance) to that of G. Our bounds are given as solutions to a maximum entropy problem on random graphs of a fixed size k that does not depend on n, under global density constraints.

Next, we study the benefits of batch learning in the overparameterized linear regression setting. Learning algorithms that divide the data into batches are prevalent in many machine-learning applications, typically offering useful trade-offs between computational efficiency and performance. Here, we examine the benefits of batch-partitioning through the lens of a minimum-norm overparameterized linear regression model with isotropic Gaussian features. We suggest a natural small-batch version of the minimum-norm estimator, and derive an upper bound on its quadratic risk, showing it is inversely proportional to the noise level as well as to the overparameterization ratio, for the optimal choice of batch size. In contrast to minimum-norm, our estimator admits a stable risk behavior that is monotonically increasing in the overparameterization ratio, eliminating both the blowup at the interpolation point and the double-descent phenomenon. Interestingly, we observe that this implicit regularization offered by the batch partition is partially explained by feature overlap between the batches. Our bound is derived via a novel combination of techniques, in particular normal approximation in the Wasserstein metric of noisy projections over random subspaces.

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