93 citations · 269 across the 18 of their papers we have counts for
6 papers · 1 filter
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu +6
An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different…
Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal +5
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nut…
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Jivat Neet Kaur, Emre Kiciman, Amit Sharma
Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algori…
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang +1
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and…
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix
Ruocheng Guo, Pengchuan Zhang, Hao Liu +1
This work considers the out-of-distribution (OOD) prediction problem where (1)~the training data are from multiple domains and (2)~the test domain is unseen in the training. DNNs f…
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer +2
It is often critical for prediction models to be robust to distributional shifts between training and testing data. From a causal perspective, the challenge is to distinguish the s…