# Active and Passive Causal Inference Learning

arXiv: https://arxiv.org/abs/2308.09248 · HTML: https://mlsanity.com/paper/2308.09248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 155 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 30 of 39)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Fast Computation of Wasserstein Barycenters](https://mlsanity.com/paper/1310.4375/) (2013)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://mlsanity.com/paper/1609.08816/) (2016)
- [Off-policy evaluation for slate recommendation](https://mlsanity.com/paper/1605.04812/) (2016)
- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
- [Desiderata for Representation Learning: A Causal Perspective](https://mlsanity.com/paper/2109.03795/) (2021)
- [Learning Weighted Representations for Generalization Across Designs](https://mlsanity.com/paper/1802.08598/) (2018)
- [Causal Imitation Learning with Unobserved Confounders](https://mlsanity.com/paper/2208.06267/) (2022)
- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models](https://mlsanity.com/paper/2102.06648/) (2021)
- [Sequential Causal Imitation Learning with Unobserved Confounders](https://mlsanity.com/paper/2208.06276/) (2022)
- [Action Centered Contextual Bandits](https://mlsanity.com/paper/1711.03596/) (2017)
- [Estimation Considerations in Contextual Bandits](https://mlsanity.com/paper/1711.07077/) (2017)
- [Estimating Individual Treatment Effects with Time-Varying Confounders](https://mlsanity.com/paper/2008.13620/) (2020)
- [Semiparametric Contextual Bandits](https://mlsanity.com/paper/1803.04204/) (2018)
- [Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation](https://mlsanity.com/paper/2210.01302/) (2022)
- [Contextual Multi-Armed Bandits for Causal Marketing](https://mlsanity.com/paper/1810.01859/) (2018)
- [Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects](https://mlsanity.com/paper/1808.03333/) (2018)
- [DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation](https://mlsanity.com/paper/2207.09920/) (2022)
- [Doubly robust treatment effect estimation with missing attributes](https://mlsanity.com/paper/1910.10624/) (2019)
- [Learning Decomposed Representation for Counterfactual Inference](https://mlsanity.com/paper/2006.07040/) (2020)
- [Random Forest for the Contextual Bandit Problem - extended version](https://mlsanity.com/paper/1504.06952/) (2015)
- [Intact-VAE: Estimating Treatment Effects under Unobserved Confounding](https://mlsanity.com/paper/2101.06662/) (2021)
- [Causal Effect Variational Autoencoder with Uniform Treatment](https://mlsanity.com/paper/2111.08656/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Brief Introduction to Causal Inference in Machine Learning](https://mlsanity.com/paper/2405.08793/) (2024)

## Abstract (arXiv)

This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference. We start by laying out an important set of assumptions that are collectively needed for causal identification, such as exchangeability, positivity, consistency and the absence of interference. From these assumptions, we build out a set of important causal inference techniques, which we do so by categorizing them into two buckets; active and passive approaches. We describe and discuss randomized controlled trials and bandit-based approaches from the active category. We then describe classical approaches, such as matching and inverse probability weighting, in the passive category, followed by more recent deep learning based algorithms. By finishing the paper with some of the missing aspects of causal inference from this paper, such as collider biases, we expect this paper to provide readers with a diverse set of starting points for further reading and research in causal inference and discovery.