The Limits of Causal Inference from Observational Data
نویسنده
چکیده
منابع مشابه
ZaliQL: Causal Inference from Observational Data at Scale
Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based framework for drawing causal inference from observational data. ZaliQL supports the state-of-the...
متن کاملCausal Inference in Anesthesia and Perioperative Observational Studies
Purpose of Review Observational studies are of great importance to anesthesia and perioperative care research, as they reflect routine clinical practice. However, because observational data are nonexperimental, assigning causality to identified relationships has a significant risk of bias. After describing the pros and cons of observational studies, we provide an overview of the different metho...
متن کاملZaliQL: 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 suit...
متن کاملTitle Methods for Graphical Models and Causal Inference
March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch Title Methods for Graphical Models and Causal Inference Description Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational ...
متن کاملCausal statistical inference in high dimensions
We present a short selective review of causal inference from observational data, with a particular emphasis on the high-dimensional scenario where the number of measured variables may be much larger than sample size. Despite major identifiability problems, making causal inference from observational data very ill-posed, we outline a methodology providing useful bounds for causal effects. Further...
متن کامل