# Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference

arXiv: https://arxiv.org/abs/2405.12340 · HTML: https://mlsanity.com/paper/2405.12340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 95 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 19 of 19)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [Influence maximization in complex networks through optimal percolation](https://mlsanity.com/paper/1506.08326/) (2015)
- [Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm](https://mlsanity.com/paper/1405.2936/) (2014)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [Reducing Interference Bias in Online Marketplace Pricing Experiments](https://mlsanity.com/paper/2004.12489/) (2020)
- [Inferring Graphs from Cascades: A Sparse Recovery Framework](https://mlsanity.com/paper/1505.05663/) (2015)
- [Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model](https://mlsanity.com/paper/2103.06392/) (2021)
- [Testing for arbitrary interference on experimentation platforms](https://mlsanity.com/paper/1704.01190/) (2017)
- [Minimizing Interference and Selection Bias in Network Experiment Design](https://mlsanity.com/paper/2004.07225/) (2020)
- [Cluster Randomized Designs for One-Sided Bipartite Experiments](https://mlsanity.com/paper/2210.16415/) (2022)
- [Using Ego-Clusters to Measure Network Effects at LinkedIn](https://mlsanity.com/paper/1903.08755/) (2019)
- [On the Aggression Diffusion Modeling and Minimization in Online Social Networks](https://mlsanity.com/paper/2005.10646/) (2020)

## Built on by (most cited first, 2 of 2)

- [Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems](https://mlsanity.com/paper/2605.25290/) (2026)
- [From Web(logs) to Web(AI): Questions, Platforms, and Methods across Twenty Editions of ICWSM](https://mlsanity.com/paper/2610.00205/) (2026)

## Abstract (arXiv)

The presence of interference, where the outcome of an individual may depend on the treatment assignment and behavior of neighboring nodes, can lead to biased causal effect estimation. Current approaches to network experiment design focus on limiting interference through cluster-based randomization, in which clusters are identified using graph clustering, and cluster randomization dictates the node assignment to treatment and control. However, cluster-based randomization approaches perform poorly when interference propagates in cascades, whereby the response of individuals to treatment propagates to their multi-hop neighbors. When we have knowledge of the cascade seed nodes, we can leverage this interference structure to mitigate the resulting causal effect estimation bias. With this goal, we propose a cascade-based network experiment design that initiates treatment assignment from the cascade seed node and propagates the assignment to their multi-hop neighbors to limit interference during cascade growth and thereby reduce the overall causal effect estimation error. Our extensive experiments on real-world and synthetic datasets demonstrate that our proposed framework outperforms the existing state-of-the-art approaches in estimating causal effects in network data.