Statistics Colloquium: Kaizheng Wang
11:30 am–12:30 pm Jones 303
Kaizheng Wang
Department of Industrial Engineering and Operations Research/Data Science Institute
Columbia University
Title: Learning to Augment Statistical Inference with Generative Models
Abstract: Generative models can produce seemingly unlimited synthetic data, yet discrepancies between synthetic and real populations can introduce bias and undermine statistical conclusions. For a new task with scarce or no real data, how much synthetic data can be safely used? This talk develops a general framework that leverages historical tasks to calibrate synthetic-data augmentation and quantify the resulting uncertainty. When no real observations are available, the framework adaptively selects the synthetic sample size to achieve nominal average-case coverage. In the application to LLM-generated survey responses, this calibrated size can be interpreted as the number of human respondents the LLM is effectively worth, providing a measure of its simulation fidelity. More generally, when real observations are available, the augmentation scheme is characterized jointly by the number of synthetic observations and the weight assigned to each. We learn a size–weight frontier that identifies the largest synthetic contribution compatible with reliable uncertainty quantification. Together, these results show how generative models can strengthen statistical analysis when real data are limited.
Statistics Colloquium: Peter McCullagh
11:30 am–12:30 pm Jones 303
Peter McCullagh
Department of Statistics
University of Chicago
Title: Causal Inference Versus Stochastic Models
Abstract: The classical statistical model is a stochastic process, which means a probability distribution on the space of outcomes. Sampling distributions and distributional properties of derived summaries follow from the process. Logical positivism in the sense of von Mises reverses the natural order, beginning with an outcome variable or kollektiv, from which probabilities are defined as limiting frequencies. Causal inference is based on the same frequency definition supplemented by random sampling. These notions of probability are in conflict, even when limiting frequencies exist. Accordingly, all derived concepts such as expectation, independence, random variable, and treatment effect are also in conflict. Broadly speaking, the frequentist concept is adequate for sample surveys and other areas of application such as auditing, where the population is concrete, finite, and accessible for simple random sampling. These conditions are not met in typical scientific research work, where the population is abstract, infinite and only partially accessible. The emphasis in this talk is on stochastic regression models, where random sampling does not arise, treatment assignment is associated with a Markov kernel, and the treatment effect is a group action on the parameter space.
Statistics Colloquium: Jing Lei
11:30 am–12:30 pm Jones 303
Jing Lei
Carnegie Mellon University
Department of Statistics & Data Science
Title: TBA
Abstract: TBA
Statistics Colloquium: Michael Sobel
11:30 am–12:30 pm Jones 303
Michael Sobel
Columbia University
Department of Statistics
Title: TBA
Abstract: TBA
Statistics Colloquium: Bin Yu
11:30 am–12:30 pm
Bin Yu
Department of Statistics, Electrical Engineering and Computer Sciences, and Center for Computational Biology
University of California, Berkeley
Title: TBA
Abstract: TBA
Statistics Colloquium: Karl Rohe
11:30 am–12:30 pm
Karl Rohe
University of Wisconsin-Madison
Department of Statistics
Title: TBA
Abstract: TBA