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<span style="margin: 0px; font-size: 15px; color: rgb(0, 0, 0); background-color: white;">The AI Lunch Series will be hosting Dr. Jundong Li (UVA) this Wednesday for a presentation on “Learning Causality with Graphs”. Additional information can be found at:<span style="margin:0px"> <a href="https://www.jlab.org/AI/lunch_series/public_events" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0" style="margin:0px">https://www.jlab.org/AI/lunch_series/public_events</a></span></span><span style="margin: 0px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;"></span><span style="margin: 0px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;"></span><span style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); display: inline !important;"></span>
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<span style="margin: 0px; font-size: 15px; color: rgb(0, 0, 0); background-color: white;">12-1PM, Wednesday, October 13, 2021</span>
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Join with Bluejeans: <a href="https://bluejeans.com/786906712/0315" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="1" style="margin:0px">https://bluejeans.com/786906712/0315</a></div>
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Abstract: “The ability to learn causality is considered a significant component of human-level intelligence and can serve as the foundation of AI. In causality learning, one fundamental problem is to understand the causal effects of a specific treatment (e.g.,
prescription of medicine) on an important outcome (e.g., cure of a disease), with significant implications in various high-impact domains such as health care, education, and e-commerce. One prevalent way to solve the problem is to directly use the observational
data since the alternative randomized experiments could be expensive, time-consuming, and even unethical in many scenarios. However, existing data-driven methods are often limited since they: (1) assume that observational data is independent and identically
distributed (i.i.d.), and (2) ignore the influence of hidden confounders (i.e., the unobserved variables that affect both the treatment and the outcome). Meanwhile, real-world data is often connected and can be abstracted as graphs (e.g., social networks,
biological networks, and knowledge graphs). Graph data is ubiquitous across many influential areas and brings opportunities to control the influence of hidden confounders and build more effective models that yield unbiased causal effects estimation. In this
talk, I will introduce our recent research efforts in causal effects learning with graphs. Specifically, we attempt to answer the following research questions: How to utilize graph information among observational data for causal effects learning? How to harness
the power of historical information to tame the influence hidden confounders for causal effects learning when the graph is continuously evolving?”<br>
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<span style="margin: 0px; font-size: 15px; color: rgb(0, 0, 0); background-color: white;">If you have questions, contact tennant@jlab.org </span><br>
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