# Estimating the effect of joint interventions from observational data in sparse high-dimensional settings

arXiv: https://arxiv.org/abs/1407.2451 · HTML: https://mlsanity.com/paper/1407.2451/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2014.

## Built on by (most cited first, 20 of 20)

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- [Model-based causal feature selection for general response types](https://mlsanity.com/paper/2309.12833/) (2023)
- [Bayesian Causal Inference with Gaussian Process Networks](https://mlsanity.com/paper/2402.00623/) (2024)
- [Causal Inference with Complex Treatments: A Survey](https://mlsanity.com/paper/2407.14022/) (2024)
- [Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs](https://mlsanity.com/paper/2411.07006/) (2024)
- [On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs](https://mlsanity.com/paper/2301.12383/) (2023)
- [BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs](https://mlsanity.com/paper/2201.12003/) (2022)
- [Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graph](https://mlsanity.com/paper/2405.15358/) (2024)
- [Lifted Causal Inference](https://mlsanity.com/paper/2606.28024/) (2026)

## Abstract (arXiv)

We consider the estimation of joint causal effects from observational data. In particular, we propose new methods to estimate the effect of multiple simultaneous interventions (e.g., multiple gene knockouts), under the assumption that the observational data come from an unknown linear structural equation model with independent errors. We derive asymptotic variances of our estimators when the underlying causal structure is partly known, as well as high-dimensional consistency when the causal structure is fully unknown and the joint distribution is multivariate Gaussian. We also propose a generalization of our methodology to the class of nonparanormal distributions. We evaluate the estimators in simulation studies and also illustrate them on data from the DREAM4 challenge.