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Full-Text Articles in Computer Sciences
Social Measurement And Causal Inference With Text, Katherine A. Keith
Social Measurement And Causal Inference With Text, Katherine A. Keith
Doctoral Dissertations
The digital age has dramatically increased access to large-scale collections of digitized text documents. These corpora include, for example, digital traces from social media, decades of archived news reports, and transcripts of spoken interactions in political, legal, and economic spheres. For social scientists, this new widespread data availability has potential for improved quantitative analysis of relationships between language use and human thought, actions, and societal structure. However, the large-scale nature of these collections means that traditional manual approaches to analyzing content are extremely costly and do not scale. Furthermore, incorporating unstructured text data into quantitative analysis is difficult due to …
Improving Evaluation Methods For Causal Modeling, Amanda Gentzel
Improving Evaluation Methods For Causal Modeling, Amanda Gentzel
Doctoral Dissertations
Causal modeling is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. Active communities of researchers in machine learning, statistics, social science, and other fields develop and enhance algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from the experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for expanding …
Using Latent Variable Models To Improve Causal Estimation, Huseyin Oktay
Using Latent Variable Models To Improve Causal Estimation, Huseyin Oktay
Doctoral Dissertations
Estimating the causal effect of a treatment from data has been a key goal for a large number of studies in many domains. Traditionally, researchers use carefully designed randomized experiments for causal inference. However, such experiments can not only be costly in terms of time and money but also infeasible for some causal questions. To overcome these challenges, causal estimation methods from observational data have been developed by researchers from diverse disciplines and increasingly studies using such methods account for a large share in empirical work. Such growing interest has also brought together two arguably separate fields: machine learning and …