Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber

Schedule

Time

• 4:00 AM - 7:00 AM August 15, 2021 SGT
• 4:00 PM - 7:00 PM August 14, 2021 EDT
• 1:00 PM - 4:00 PM August 14, 2021 PDT

Live Zoom Link

To be shared within the KDD 21 Virtual Platform during the conference.

Abstract

In recent years, both academic research and industry applications see an increased effort in using machine learning methods to measure granular causal effects and design optimal policies based on these causal estimates. Open source packages such as CausalML and EconML provide a unified interface for applied researchers and industry practitioners with a variety of machine learning methods for causal inference. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases.

Target Audience and Prerequisites for the Tutorial

Anyone who is interested in causal inference and machine learning, especially economists/statisticians/data scientists who want to learn how to combine causal inference and machine learning with real industry use cases incorporated in large scaled machine learning systems at companies such as Microsoft, TripAdvisor and Uber. The tutorial assumes some basic knowledge in statistical methods, machine learning algorithms and the Python programming language.

Outline

Presentation Abstracts

Introduction to Causal Inference

We will give an overview of basic concepts in causal inference. A quick refresher on the main tools and terminology of causal inference: correlation vs causation, average, conditional, and individual treatment effects, causal inference via randomization, Causal inference using instrumental variables, Causal inference via unconfoundedness.

Introduction to CasualML

We will provide an overview of CausalML, an open source Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research. We will introduce the main components of CausalML: (1) inference with causal machine learning algorithms (e.g. meta-learners, uplift trees, CEVAE, dragonnet), (2) validation/analysis methods (e.g. synthetic data generation, AUUC, sensitivity analysis, interpretability), (3) optimization methods (e.g. policy optimization, value optimization, unit selection).

Case #1: Causal Impact Analysis with Observational Data at Uber

As an introductory case study for using causal inference, we will cover the use case of understanding the causal impact from observational data in the context of cross sell at Uber. We emphasize that simple comparisons of users who make cross purchase or not will produce biased estimates and that can be demonstrated in the causal inference framework. We show the use of different causal estimation methodologies through propensity score matching and meta learners to estimate the causal impact. In addition, we will use sensitivity analysis to show the robustness of the estimates.

Case #2: Targeting Optimization: Bidder at Uber

We will introduce the audience selection method with uplift modeling in online RTB, which aims to estimate heterogeneous treatment effects for advertising. It has been studied to provide a superior return on investment by selecting the most incremental users for a specific campaign. To examine the effectiveness of uplift modeling in the context of real-time bidding, we conducted the comparative analysis of four different meta-learners on real campaign data. We adapted an explore-exploit set up for offline training and online evaluation. We will also introduce how we use Targeted Maximum Likelihood Estimation (TMLE) based Average Treatment Effect (ATE) as ground truth for evaluation.

Introduction to EconML

We will provide an overview of recent methodologies that combine machine learning with causal inference and the significant statistical power that machine learning brings to causal inference estimation methods. We will outline the structure and capabilities of the EconML package and describe some of the key causal machine learning methodologies that are implemented (e.g. double machine learning, causal forests, deepiv, doubly robust learning, dynamic double machine learning). We will also outline approaches to confidence interval construction (e.g. bootstrap, bootstrap-of-little-bags, debiased lasso), interpretability (shap values, tree interpreters) and policy learning (doubly robust policy learning).

Case #3: Customer Segmentation at TripAdvisor with Recommendation A/B Tests

We examine the scenario in which we wish to learn heterogeneous treatment effects (CATE), but observational data is biased and direct experimental data (e.g. A/B test) is plagued by imperfect compliance. In this setup, TripAdvisor would like to know whether joining a membership program compels users to spend more time engaging with the website and purchasing more products. The usual approach, a direct A/B test, is infeasible: the website cannot force users to comply and become members, hence the imperfect compliance that can bias calculations. The solution is to use an alternative A/B test that was originally designed to measure whether an easier sign-up process would promote user membership. This A/B test plays the role of an instrument that nudges users to sign up for membership. We introduce EconML’s IntentToTreatDRIV estimator which can leverage this repurposed A/B test to both learn the effect of membership on user engagement and understand how these effects vary with customer features. We show how this novel methodology led to extracting key business insights and helped TripAdvisor understand and differentiate how customers engage with their platform.

Case #4: Long-Term Return-on-Investment at Microsoft via Short-Term Proxies

In this case study, we talk about using observational data to measure the long term Return-on-Investment of some types of dollar value investments Microsoft gives to the enterprise customers. There are many challenges for this setting, for instance, we don't have enough period of data to identify a long term ROI, we should control the effect coming from the future investment and we are in a high dimensional data space. We then propose a surrogate based approach assuming the long-term effect is channeled through some short-term proxies and employ a dynamic adjustment to the surrogate model in order to get rid of the effect from future investment, finally apply double machine learning (DML) techniques to estimate the ROI. We apply this methodology to answer the questions like what is the average long-run ROI on each type of the investment? What types of customers have a higher ROI to a specific investment? And how different incentives impact the different solution areas. Finally we will showcase how you could use EconML to solve similar problems by only a few lines of code.

Tutors

Presenters

• Jing Pan, Uber, CausalML
• Yifeng Wu, Uber, CausalML
• Huigang Chen, Facebook, CausalML
• Totte Harinen, Toyota Research Institute, CausalML
• Paul Lo, Uber, CausalML
• Greg Lewis, Microsoft Research, EconML
• Vasilis Syrgkanis, Microsoft Research, EconML
• Miruna Oprescu, Microsoft Research, EconML
• Maggie Hei, Microsoft Research, EconML

Contributors

• Jeong-Yoon Lee, Netflix, CausalML
• Zhenyu Zhao, Tencent, CausalML
• Keith Battocchi, Microsoft Research, EconML
• Eleanor Dillon, Microsoft Research, EconML

References

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