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2021

Bounds on Causal Effects and Application to High Dimensional Data

Li, Ang, Pearl, Judea

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This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed.

For such scenarios, we derive bounds on the causal effects by solving two non-linear optimization problems, and demonstrate that the bounds are sufficient.
Using this optimization method, we propose a framework for dimensionality reduction that allows one to trade bias for estimation power, and demonstrate its performance using simulation studies.

Bounds on Causal Effects and Application to High Dimensional Data · Around

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Fundamental research statistics for the behavioral sciences
John T. Roscoe · 1975
Earlier work this paper cites.
A software package for sequential quadratic programming
Dieter Kraft · 1988
Earlier work this paper cites.
Causal diagrams for empirical research
Judea Pearl · 1995
Earlier work this paper cites.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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