arXiv subjects

Marc Ratkovic

Publications and source records attributed to Marc Ratkovic.

3 recordsLinked to original sources

arXiv2025

Large Language Models for Statistical Inference: Context Augmentation with Applications to the Two-Sample Problem and Regression

We introduce context augmentation, a data-augmentation approach that uses large language models (LLMs) to generate contexts around observed strings as a means of facilitating valid frequentist inference. These generated contexts serve to reintroduce uncertainty, incorporate auxiliary information, and facilitate interpretability. For example, in the two-sample test, we compare the log-probability of strings under contexts from its own versus the other group. We show on synthetic data that the method's t-statistics exhibit the expected null behaviour while maintaining power and, through a replication, that the method is powerful and interpretable. We next introduce text-on-text regression. Contexts generated around the predictor string are treated as mediating variables between the predictor and outcome strings. Using negative controls, we then distinguish between semantic and syntactic dimensions of prediction. Analysis of real-world dialogic data illustrates behaviour predicted from a psycholinguistic framework. Theoretically, we provide identification conditions, derive an influence-function decomposition, and show that repeated cross-fitting of a pivotal statistic yields higher-order efficiency. We derive bounds linking estimation error, context count, and number of cross-fits. Taken together, context augmentation offers the ability to connect LLMs with longstanding statistical practice.

stat.ME↗

arXiv2017

Estimation and Inference on Nonlinear and Heterogeneous Effects

Multiple regression has been the go-to method for data analysis for generations of scholars due to its transparency, interpretability, and desirable theoretical properties. However, the method's simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level effect of a treatment variable. Importantly, we introduce a robust approach to uncertainty estimates. We provide simulation evidence and an application illustrating the performance of the method.

stat.ML↗

arXiv2013

Estimating treatment effect heterogeneity in randomized program evaluation

When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers must investigate when the treatments do and do not work. Indeed, the estimation of treatment effect heterogeneity plays an essential role in (1) selecting the most effective treatment from a large number of available treatments, (2) ascertaining subpopulations for which a treatment is effective or harmful, (3) designing individualized optimal treatment regimes, (4) testing for the existence or lack of heterogeneous treatment effects, and (5) generalizing causal effect estimates obtained from an experimental sample to a target population. In this paper, we formulate the estimation of heterogeneous treatment effects as a variable selection problem. We propose a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. The proposed method is motivated by and applied to two well-known randomized evaluation studies in the social sciences. Our method selects the most effective voter mobilization strategies from a large number of alternative strategies, and it also identifies the characteristics of workers who greatly benefit from (or are negatively affected by) a job training program. In our simulation studies, we find that the proposed method often outperforms some commonly used alternatives.

stat.AP↗