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Found 6 papers, 1 papers with code
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining.
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns.
Causal Discovery from Temporal Data: An Overview and New Perspectives
Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery.
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution
Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions.
Nature of $X(6900)$ and its production mechanism at LHCb
We show that the Pomeron exchanges play a unique role in vector charmonium scatterings.
High Energy Physics - Phenomenology
