Title: Causal Claims (joint work with Prashant Garg (Imperial)).

Abstract: Economic research has experienced a profound shift towards establishing causal relationships using an ever expanding suite of empirical methods. Despite this "credibility revolution," there is limited comprehensive analysis of how these methods and causal claims have evolved across economics subfields. This paper addresses this gap by analyzing over 44,000 NBER and CEPR working papers using a custom large language model to extract structured information on authorship, empirical methods, underlying data and associated causal claims. We use this data to document the empiricisation of economics research with a significant growth in the use of methods like Difference-in-Differences, Instrumental Variables, and Randomized Controlled Trials, alongside an increase in the number of causal claims that are made and evidenced. Yet, nearly 30% of causal claims remain unsupported by rigorous identification strategies. We also observe rising narrative complexity and increased use of private data, raising concerns about transparency, replicability and property rights attached to knowledge goods that are produced using private data. Our findings highlight the evolving landscape of empirical economics, emphasizing the need for continued focus on methodological rigor and data accessibility. This study contributes to a better understanding of research practices in economics and informs efforts to enhance the credibility and transparency of the discipline.