Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface

09/08/2022

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Strong untestable assumptions are almost universal in causal point estimation. In particular settings, bounds can be derived to narrow the possible range of a causal effect. Symbolic bounds apply to all settings that can be depicted using the same directed acyclic graph (DAG) and for the same effect of interest. Although the core of the methodology for deriving symbolic bounds has been previously developed, the means of implementation and computation have been lacking. Our R-package causaloptim aims to solve this usability problem by implementing the method of Sachs et al. (2022a) and providing the user with a graphical interface through shiny that allows for input in a way that most researchers with an interest in causal inference will be familiar; a DAG (via a point-and-click experience) and specifying a causal effect of interest using familiar counterfactual notation.

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Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface

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Accessible Computation of Tight Symbolic Bounds on Causal Effects using an Intuitive Graphical Interface

Related Research

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Nonparametric bounds for causal effects in imperfect randomized experiments

Efficient Computation of Counterfactual Bounds

Causal Mechanism-based Model Construction

Ananke: A Python Package For Causal Inference Using Graphical Models

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