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X-WR-CALDESC:Events for CREST
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DTSTART:20260329T010000
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DTSTART:20261025T010000
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DTSTART;TZID=Europe/Helsinki:20260126T121500
DTEND;TZID=Europe/Helsinki:20260126T133000
DTSTAMP:20260712T011616
CREATED:20251222T112708Z
LAST-MODIFIED:20260121T151202Z
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SUMMARY:Maksim SMIRNOV (CERGE-EI) "Treatment effects identification and testing via reduced-form projections"
DESCRIPTION:Macro Seminar\nTime : 12h15 – 13h30 \nDate : 26 th  January 2026 \nSalle 3001 \nMaksim Smirnov (CERGE-EI) “Treatment effects identification and testing via reduced-form projections” \nAbstract: I study a nonparametric instrumental variable (IV) model with a binary treatment and develop new methods for testing treatment effect heterogeneity. In particular\, I propose tests for (i) constant marginal treatment effects (MTE) and (ii) monotone decreasing MTE curves. The analysis builds on a novel identification result showing that the average second derivative of a regression function can be recovered via a quadratic projection. This result enables identification of the average slope of the MTE curve through a reduced-form projection. Building on these insights\, I construct simple\, projection-based tests for constant and monotone MTEs that are easy to implement and have direct implications for policy evaluation and welfare maximization. Monte Carlo simulations and an empirical application demonstrate the tests’ finite-sample performance and practical relevance.  \n  \n  \n
URL:https://crest.science/event/maksim-smirnov-lse-t-b-a/
CATEGORIES:Macroeconomics,Seminars
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DTSTART;TZID=Europe/Helsinki:20260126T140000
DTEND;TZID=Europe/Helsinki:20260126T153000
DTSTAMP:20260712T011616
CREATED:20251208T145925Z
LAST-MODIFIED:20260121T101349Z
UID:18636-1769436000-1769441400@crest.science
SUMMARY:Eric MOULINES (EPITA & MBZUAI) - Sampling Posteriors with Implicit Priors: Diffusion Models\, Guidance Bias\, and SMC
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 26th January\nPlace: 3001 \n  \nEric MOULINES (EPITA & MBZUAI) – Sampling Posteriors with Implicit Priors: Diffusion Models\, Guidance Bias\, and SMC \n  \n Abstract:  \n  \nWe study Bayesian sampling for inverse problems where the goal is to reconstruct a signal $X$ from noisy observations $Y=A(X)+\sigma Z$ while providing uncertainty quantification. The posterior can be written as $\pi(dx)\propto g(x)\rho(dx)$\, where $g$ is a potential (typically the likelihood) and $\rho$ is an implicit prior approximated by a diffusion model (DDPM). This setting makes standard approaches that rely on evaluating the prior density intractable. We review guidance methods that modify the denoising dynamics using score-based terms (notably via Tweedie’s formula)\, and we highlight the biases introduced by approximating intermediate potentials. We then present a “sequence of distributions” viewpoint: the exact posterior path is replaced by a chain of distributions bridging a reference Gaussian to the target\, described through a Feynman–Kac representation and simulable with SMC/particle methods. Finally\, we discuss Monte Carlo guidance schemes and mixture-based approaches\, including a data-augmentation formulation and a deterministic Gibbs sampler\, and we illustrate these ideas on imaging tasks (super-resolution\, deblurring\, inpainting) and audio source separation. \n  \n  \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST)\, Jaouad MOURTADA (CREST) \n  \n  \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/eric-moulines-epita-mbzuai-tba/
CATEGORIES:Seminars,Statistics
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