Causal Inference and the Data-fusion Problem
DSA ADS Course - 2021
Causal Inference, Data-fusion, Causal Analysis, Causal Data Science, Counterfactuals, Selection Bias
Discuss concepts and techniques of different approaches to causal analysis, the curse of big data, data fusion and bias challenges. Discuss biases such as: confounding, sampling selection, and cross-population biases, along with a general, potential mitigation nonparametric framework for handling biases.
Discuss appropriate use of counterfactuals in causal analysis and risk of reasonable inference vs. unreasonable inference.
Causal Inference and the Data-fusion Problem - July, 2016
We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion— piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks.