A Class of Algorithms for General Instrumental Variable Models
- URL: http://arxiv.org/abs/2006.06366v3
- Date: Wed, 21 Oct 2020 14:41:32 GMT
- Title: A Class of Algorithms for General Instrumental Variable Models
- Authors: Niki Kilbertus, Matt J. Kusner, Ricardo Silva
- Abstract summary: Causal treatment effect estimation is a key problem that arises in a variety of real-world settings.
We provide a method for causal effect bounding in continuous distributions.
- Score: 29.558215059892206
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Causal treatment effect estimation is a key problem that arises in a variety
of real-world settings, from personalized medicine to governmental policy
making. There has been a flurry of recent work in machine learning on
estimating causal effects when one has access to an instrument. However, to
achieve identifiability, they in general require one-size-fits-all assumptions
such as an additive error model for the outcome. An alternative is partial
identification, which provides bounds on the causal effect. Little exists in
terms of bounding methods that can deal with the most general case, where the
treatment itself can be continuous. Moreover, bounding methods generally do not
allow for a continuum of assumptions on the shape of the causal effect that can
smoothly trade off stronger background knowledge for more informative bounds.
In this work, we provide a method for causal effect bounding in continuous
distributions, leveraging recent advances in gradient-based methods for the
optimization of computationally intractable objective functions. We demonstrate
on a set of synthetic and real-world data that our bounds capture the causal
effect when additive methods fail, providing a useful range of answers
compatible with observation as opposed to relying on unwarranted structural
assumptions.
Related papers
- Doubly Robust Proximal Causal Learning for Continuous Treatments [56.05592840537398]
We propose a kernel-based doubly robust causal learning estimator for continuous treatments.
We show that its oracle form is a consistent approximation of the influence function.
We then provide a comprehensive convergence analysis in terms of the mean square error.
arXiv Detail & Related papers (2023-09-22T12:18:53Z) - B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under
Hidden Confounding [51.74479522965712]
We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding.
We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
arXiv Detail & Related papers (2023-04-20T18:07:19Z) - Data-Driven Influence Functions for Optimization-Based Causal Inference [105.5385525290466]
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing.
We study the case where probability distributions are not known a priori but need to be estimated from data.
arXiv Detail & Related papers (2022-08-29T16:16:22Z) - Deep Learning Methods for Proximal Inference via Maximum Moment
Restriction [0.0]
We introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding.
Our method achieves state of the art performance on two well-established proximal inference benchmarks.
arXiv Detail & Related papers (2022-05-19T19:51:42Z) - The Causal Marginal Polytope for Bounding Treatment Effects [9.196779204457059]
We propose a novel way to identify causal effects without constructing a global causal model.
We enforce compatibility between marginals of a causal model and data, without constructing a global causal model.
We call this collection of locally consistent marginals the causal marginal polytope.
arXiv Detail & Related papers (2022-02-28T15:08:22Z) - Stochastic Causal Programming for Bounding Treatment Effects [8.879868078611443]
Causal effect estimation is important for many tasks in the natural and social sciences.
We use flexible learning algorithms and Monte Carlo methods to implement a family of solutions under the name of causal programming.
arXiv Detail & Related papers (2022-02-22T10:55:24Z) - Partial Identification with Noisy Covariates: A Robust Optimization
Approach [94.10051154390237]
Causal inference from observational datasets often relies on measuring and adjusting for covariates.
We show that this robust optimization approach can extend a wide range of causal adjustment methods to perform partial identification.
Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.
arXiv Detail & Related papers (2022-02-22T04:24:26Z) - Algorithmic Recourse in Partially and Fully Confounded Settings Through
Bounding Counterfactual Effects [0.6299766708197883]
Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system.
Existing methods compute the effect of recourse actions using a causal model learnt from data under the assumption of no hidden confounding and modelling assumptions such as additive noise.
We propose an alternative approach for discrete random variables which relaxes these assumptions and allows for unobserved confounding and arbitrary structural equations.
arXiv Detail & Related papers (2021-06-22T15:07:49Z) - BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable.
We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
arXiv Detail & Related papers (2021-06-07T10:14:18Z) - Differentiable Causal Discovery from Interventional Data [141.41931444927184]
We propose a theoretically-grounded method based on neural networks that can leverage interventional data.
We show that our approach compares favorably to the state of the art in a variety of settings.
arXiv Detail & Related papers (2020-07-03T15:19:17Z) - Differentiable Causal Backdoor Discovery [36.68511018339594]
We present an algorithm that exploits auxiliary variables, similar to instruments, in order to find an appropriate adjustment by a gradient-based optimization method.
We demonstrate that it outperforms practical alternatives in estimating the true causal effect, without knowledge of the full causal graph.
arXiv Detail & Related papers (2020-03-03T11:32:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.