Published in Uncertainty in Artificial Intelligence (UAI), 2025
We show that even if the causal graph is partially known, we can identify the root-causes with a linear number of invariance tests. This is the first known result on incorporating a partial causal structure for root cause analysis.
Published in Uncertainty in Artificial Intelligence (UAI), 2025
Recent research proposes to limit the conditioning set size for robust causal discovery. However, the existing algorithms require exhaustive testing of all CI relations with conditioning set sizes up to a certain integer $k$. This becomes problematic in practice when variables with large support are present, as it makes CI tests less reliable due to near-deterministic relationships, thereby violating the faithfulness assumption. To address this issue, we propose a causal discovery algorithm that only uses CI tests where the conditioning sets are restricted to a given set of conditioning sets including the empty set.
Published in Uncertainty in Artificial Intelligence (UAI), 2023
In this work, we first provide a graphical characterization of the identifiability of conditional causal queries. Next, we leverage this characterization together with a greedy search step to develop a polynomial-time algorithm for finding invariant predictors using the causal graph. Given the correct causal graph, our method is guaranteed to find at least one invariant predictor, if it exists. We show that our proposed algorithm can significantly reduce the run-time both in simulated and semi-synthetic data experiments and have predictive performance that is comparable to the existing work that runs in exponential time.
Published in Proceedings of the National Academy of Sciences (PNAS), 2021
To study the COVID-19 pandemic, its effects on society, and measures for reducing its spread, researchers need detailed data on the course of the pandemic. Standard public health data streams suffer inconsistent reporting and frequent, unexpected revisions. They also miss other aspects of a population’s behavior that are worthy of consideration. We present an open database of COVID signals in the United States, measured at the county level and updated daily. This includes traditionally reported COVID cases and deaths, and many others: measures of mobility, social distancing, internet search trends, self-reported symptoms, and patterns of COVID-related activity in deidentified medical insurance claims. The database provides all signals in a common, easy-to-use format, empowering both public health research and operational decision-making.
Published in IEEE Conference on Control Technology and Applications (CCTA), 2017
In this work we apply a systems-theoretic approach to identifying trend setters on Twitter. A network reconstruction algorithm was applied to Twitter data to determine causal relationships among topics discussed by popular Twitter users.
Published in 9th Causal Inference Workshop at UAI, 2024
In causal discovery, the constraint-based approaches often rely on an assumption known as faithfulness/stability, only the variables that are d-separated in a directed acyclic graph will be statistically independent. This assumption can be partitioned into two subconditions: orientation faithfulness and adjacency faithfulness. Under adjacency faithfulness, a sound algorithm known as CPC, a conservative version of PC algorithm, has been developed and is conjectured to be complete. In this work, we show that the CPC algorithm is not complete and propose two new sound orientation rules as part of a sound causal discovery algorithm called revised CPC (RCPC) under orientation unfaithfulness.