Causal Inference via Sparse Additive Models with Application to Online Advertising
Authors: Wei Sun, Pengyuan Wang, Dawei Yin, Jian Yang, Yi Chang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the efficacy of our approach, we apply it to a real online advertising campaign to evaluate the impact of three ad treatments: ad frequency, ad channel, and ad size. We show that the ad frequency usually has a treatment effect cap when ads are showing on mobile device. In addition, the strategies for choosing best ad size are completely different for mobile ads and online ads. |
| Researcher Affiliation | Collaboration | 1Purdue University, West Lafayette, IN,USA, sun244@purdue.edu 2Yahoo Labs, Sunnyvale, CA, USA, {pengyuan, daweiy, jianyang,yichang}@yahoo-inc.com |
| Pseudocode | Yes | Table 1: Our Two-stage Algorithm Input: Yi, Xi, Ti for i = 1, 2, ...N. Output: Estimated treatment effect for t. Stage 1: Obtain the estimated propensity parameter bθ(Xi) by modeling Ti|Xi via SAM. Stage 2: Calculate the final treatment effect by modeling Yi|Ti, bθ(Xi) via GAM as in (10). |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of source code for the methodology. |
| Open Datasets | No | The reported dataset and results are deliberately incomplete and subject to anonymization, and thus do not necessarily reflect the real portfolio at any particular time. |
| Dataset Splits | No | The paper does not provide specific details on validation splits or cross-validation setup for reproducibility. It mentions a testing data for simulation, but not a distinct validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various algorithms (gbm, lasso, slogit, bagging, rf, SAM, GAM) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions parameters like “sample size N = 1000 and number of features p = 200” for simulations, and describes how the tuning parameter λ can be tuned, but does not provide concrete hyperparameter values or detailed training configurations (e.g., learning rate, batch size) for the models used in the experiments. |