# Ads Supply Personalization via Doubly Robust Learning

arXiv: https://arxiv.org/abs/2410.12799 · HTML: https://mlsanity.com/paper/2410.12799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 29 Sep 2024.

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

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [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)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks](https://mlsanity.com/paper/1810.00656/) (2018)
- [DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems](https://mlsanity.com/paper/1909.03602/) (2019)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [Deep Counterfactual Networks with Propensity-Dropout](https://mlsanity.com/paper/1706.05966/) (2017)
- [Explicit Feature Interaction-aware Uplift Network for Online Marketing](https://mlsanity.com/paper/2306.00315/) (2023)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor](https://mlsanity.com/paper/2206.02620/) (2022)
- [Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.00377/) (2022)
- [Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach](https://mlsanity.com/paper/2105.13556/) (2021)
- [Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.11589/) (2022)

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

- [Causal Predictive Optimization and Generation for Business AI](https://mlsanity.com/paper/2505.09847/) (2025)
- [Adaptive Ad Load Design for Sponsored Search Markets: Evidence, Theory, and Deployment](https://mlsanity.com/paper/2607.14418/) (2026)

## Abstract (arXiv)

Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.