ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data
Causal inference from observational data is a subject of active research and development in statistics and computer science. Many toolkits have been developed for this purpose that depends on statistical software. However, these toolkits do not scale to large datasets. In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL. This suite supports the state-of-the-art methods for causal inference and run at scale within a database engine. In addition, we introduce several optimization techniques that significantly speedup causal inference, both in the online and offline setting. We evaluate the quality and performance of our techniques by experiments of real datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts
We examine the role of textual data as study units when conducting causal inference by drawing parallels between human subjects and organized texts. %in human population research. We elaborate on key causal concepts and …
Causal InferenceGenerating Synthetic Text Data to Evaluate Causal Inference Methods
Drawing causal conclusions from observational data requires making assumptions about the true data-generating process. Causal inference research typically considers low-dimensional data, such as categorical or numerical …
Causal InferenceText GenerationComparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
The aim of this paper is to make clear and precise the relationship between the Rubin causal model (RCM) and structural causal model (SCM) frameworks for causal inference. Adopting a neutral logical perspective, and draw…
Causal InferenceThe Causal Frame Problem: An Algorithmic Perspective
The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ability to make decisions on the basis onl…
Causal JudgmentPhilosophyLarge Language Models as Co-Pilots for Causal Inference in Medical Studies
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studi…
Causal InferenceSelection bias