Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis

By Pengyuan Wang, Wei Sun, Dawei Yin, Jian Yang, Yi Chang. 2015

42 citations recorded by the source corpus.

Subfields: Marketing, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Marketing

1. Sheng Li
2. Yun Raymond Fu

In Statistics and Probability

1. Jing Gao
2. Aidong Zhang
3. Zhixuan Chu
4. Yaliang Li
5. Mengdi Huai
6. Sheng Li
7. Liuyi Yao
8. Yun Raymond Fu

Citations in the corpus

• Representation Learning for Treatment Effect Estimation from Observational Data (2018, background): “Estimating the causal effect of an intervention/treatment at individual-level is an important problem that can benefit many domains including health care [12, 1], digital marketing [6, 34, 24], and machine learning [10, 37, 21, 23, 20].”
• Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, background): “Causal questions exist in many areas, such as health care [24, 12], economics [14], political science [17], education [36], digital marketing [6, 43, 5, 15, 44], etc.”
• A Survey on Causal Inference (2020, methodology): “ed by comparing the potential outcome of the treatment with different values and the baseline treatment. To estimate the potential outcome of treatment with multidimensional values, tree-based method [142] and sparse additive model based method [132] are proposed to enable the comparison between pot”