# Continual Causal Inference with Incremental Observational Data

arXiv: https://arxiv.org/abs/2303.01775 · HTML: https://mlsanity.com/paper/2303.01775/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Class-incremental Learning via Deep Model Consolidation](https://mlsanity.com/paper/1903.07864/) (2019)
- [Memory-Efficient Incremental Learning Through Feature Adaptation](https://mlsanity.com/paper/2004.00713/) (2020)
- [Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks](https://mlsanity.com/paper/1702.05870/) (2017)
- [Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks](https://mlsanity.com/paper/1810.00656/) (2018)
- [Learning Individual Causal Effects from Networked Observational Data](https://mlsanity.com/paper/1906.03485/) (2019)
- [Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data](https://mlsanity.com/paper/2106.02881/) (2021)
- [Matching in Selective and Balanced Representation Space for Treatment Effects Estimation](https://mlsanity.com/paper/2009.06828/) (2020)
- [Causal Effect Estimation: Recent Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2302.00848/) (2023)
- [Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data](https://mlsanity.com/paper/2202.10885/) (2022)
- [Group Average Treatment Effects for Observational Studies](https://mlsanity.com/paper/1911.02688/) (2019)

## Built on by (most cited first, 13 of 13)

- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
- [Prompt-augmented Temporal Point Process for Streaming Event Sequence](https://mlsanity.com/paper/2310.04993/) (2023)
- [pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2305.11304/) (2023)
- [Continual Learning in Predictive Autoscaling](https://mlsanity.com/paper/2307.15941/) (2023)
- [Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations](https://mlsanity.com/paper/2407.03082/) (2024)
- [Overcoming Growth-Induced Forgetting in Task-Agnostic Continual Learning](https://mlsanity.com/paper/2408.10566/) (2024)
- [C2TC: A Training-Free Framework for Efficient Tabular Data Condensation](https://mlsanity.com/paper/2602.21717/) (2026)
- [Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects](https://mlsanity.com/paper/2403.02624/) (2024)
- [Towards Real-world Debiasing: Rethinking Evaluation, Challenge, and Solution](https://mlsanity.com/paper/2405.15240/) (2024)
- [The Estimation of Continual Causal Effect for Dataset Shifting Streams](https://mlsanity.com/paper/2504.20471/) (2025)
- [Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding](https://mlsanity.com/paper/2511.01228/) (2025)
- [Ca-MCF: Category-level Multi-label Causal Feature selection](https://mlsanity.com/paper/2602.12961/) (2026)
- [When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning](https://mlsanity.com/paper/2609.15305/) (2026)

## Abstract (arXiv)

The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public policy, marketing campaigns, and so on, which facilitates the development of causal effect estimation. Although significant advances have been made to overcome the challenges in the academic area, such as missing counterfactual outcomes and selection bias, they only focus on source-specific and stationary observational data, which is unrealistic in most industrial applications. In this paper, we investigate a new industrial problem of causal effect estimation from incrementally available observational data and present three new evaluation criteria accordingly, including extensibility, adaptability, and accessibility. We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions. Instead of having access to all seen observational data, our method only stores a limited subset of feature representations learned from previous data. Combining selective and balanced representation learning, feature representation distillation, and feature transformation, our method achieves the continual causal effect estimation for new data without compromising the estimation capability for original data. Extensive experiments demonstrate the significance of continual causal effect estimation and the effectiveness of our method.