DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution

arXiv:2401.08875 · cs.LG, cs.AI, stat.ME · Submitted 2024-01-16 · Read on arXiv

cs.LG, cs.AI, stat.ME

Submitted: 2024-01-16

Updated: 2026-09-22

Terminology

• Multi-Touch Attribution — 13× in this paper
• Representation Learning — 10× in this paper · explained in 2 episodes
• Attention Mechanism — 8× in this paper · explained in 3 episodes
• AUC — 7× in this paper · explained in 2 episodes
• Loss Function — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Feature Extraction — 6× in this paper · explained in 2 episodes
• Causal Attention Module — 5× in this paper
• Causal Inference — 5× in this paper · explained in 4 episodes
• Activation Function — 4× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Shapley values — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Cross Entropy — 2× in this paper
• Area Under Curve — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Cost Per Action — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Deep Neural Net with Attention for Multi-channel Multi-touch Attribution
• Interpretable Deep Learning Model for Online Multi-touch Attribution
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
• Representation Learning via Invariant Causal Mechanisms
• Empirical Bernstein Bounds and Sample Variance Penalization

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