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Regression Discontinuity Design with Spillovers
Eric Auerbach, Yong Cai, Ahnaf Rafi
https://arxiv.org/abs/2404.06471 https://arxiv.org/pdf/2404.06471
arXiv:2404.06471v1 Announce Type: new
Abstract: Researchers who estimate treatment effects using a regression discontinuity design (RDD) typically assume that there are no spillovers between the treated and control units. This may be unrealistic. We characterize the estimand of RDD in a setting where spillovers occur between units that are close in their values of the running variable. Under the assumption that spillovers are linear-in-means, we show that the estimand depends on the ratio of two terms: (1) the radius over which spillovers occur and (2) the choice of bandwidth used for the local linear regression. Specifically, RDD estimates direct treatment effect when radius is of larger order than the bandwidth, and total treatment effect when radius is of smaller order than the bandwidth. In the more realistic regime where radius is of similar order as the bandwidth, the RDD estimand is a mix of the above effects. To recover direct and spillover effects, we propose incorporating estimated spillover terms into local linear regression -- the local analog of peer effects regression. We also clarify the settings under which the donut-hole RD is able to eliminate the effects of spillovers.

Apprentices to Research Assistants: Advancing Research with Large Language Models
M. Namvarpour, A. Razi
https://arxiv.org/abs/2404.06404 https://

Apprentices to Research Assistants: Advancing Research with Large Language Models
Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for m…

scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
Shengze Dong, Zhuorui Cui, Ding Liu, Jinzhi Lei
https://arxiv.org/abs/2404.06153

scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
Motivation: Single-cell RNA sequencing (scRNA-seq) is a groundbreaking technology extensively utilized in biological research, facilitating the examination of gene expression at the individual cell level within a given tissue sample. While numerous tools have been developed for scRNA-seq data analysis, the challenge persists in capturing the distinct features of such data and replicating virtual datasets that share analogous statistical properties. Results: Our study introduces a generative app…

Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities
Cong Cao, Ramit Debnath, R. Michael Alvarez
https://arxiv.org/abs/2405.04716 …

Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities
Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and ai…

Hypothesis Generation with Large Language Models
Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, Chenhao Tan
https://arxiv.org/abs/2404.04326 …

Hypothesis Generation with Large Language Models
Effective generation of novel hypotheses is instrumental to scientific progress. So far, researchers have been the main powerhouse behind hypothesis generation by painstaking data analysis and thinking (also known as the Eureka moment). In this paper, we examine the potential of large language models (LLMs) to generate hypotheses. We focus on hypothesis generation based on data (i.e., labeled examples). To enable LLMs to handle arbitrarily long contexts, we generate initial hypotheses from a sm…

Interesting work on food systems here: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=7329

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Anirban Mukherjee, Hannah Hanwen Chang
https://arxiv.org/abs/2404.04979 https://arxiv.org/pdf/2404.04979
arXiv:2404.04979v1 Announce Type: new
Abstract: Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that assume values in a high-dimensional ambient space but are sampled from an underlying manifold. Our theoretical and numerical analyses outline challenges posed by such categorical variables in causal inference. Specifically, dynamically varying and sparse levels can lead to violations of the Donsker conditions and a failure of the estimation functionals to converge to a tight Gaussian process. Traditional approaches, including the exclusion of rare categorical levels and principled variable selection models like LASSO, fall short. CAVIAR embeds the data into a lower-dimensional global coordinate system. The mapping can be derived from both structured and unstructured data, and ensures stable and robust estimates through dimensionality reduction. In a dataset of direct-to-consumer apparel sales, we illustrate how high-dimensional categorical variables, such as zip codes, can be succinctly represented, facilitating inference and analysis.

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
Anirban Mukherjee, Hannah Hanwen Chang
https://arxiv.org/abs/2404.04979 https://arxiv.org/pdf/2404.04979
arXiv:2404.04979v1 Announce Type: new
Abstract: Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that assume values in a high-dimensional ambient space but are sampled from an underlying manifold. Our theoretical and numerical analyses outline challenges posed by such categorical variables in causal inference. Specifically, dynamically varying and sparse levels can lead to violations of the Donsker conditions and a failure of the estimation functionals to converge to a tight Gaussian process. Traditional approaches, including the exclusion of rare categorical levels and principled variable selection models like LASSO, fall short. CAVIAR embeds the data into a lower-dimensional global coordinate system. The mapping can be derived from both structured and unstructured data, and ensures stable and robust estimates through dimensionality reduction. In a dataset of direct-to-consumer apparel sales, we illustrate how high-dimensional categorical variables, such as zip codes, can be succinctly represented, facilitating inference and analysis.

This https://arxiv.org/abs/2402.15585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Inference for Regression with Variables Generated from Unstructured Data
The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why this two-step strategy leads to biased inference in empirically plausible settings. More constructively, we propose a one-step strategy for valid inference that uses the upstream and downstream models jo…