Durai Rajamanickam
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Researcher
Faculty Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Durai Rajamanickam's research centers on causal inference and its applications, particularly within deep learning frameworks. His recent publications explore foundational concepts like backdoor and frontdoor criteria, causal graphs, and propensity scores, indicating a focus on establishing rigorous methods for understanding cause-and-effect relationships in complex systems. He investigates the integration of causal reasoning into deep learning models, with specific work on balancing representations using techniques such as CFRNet (Causal Representation Learning Network).
His scholarship extends to the theoretical underpinnings of causal inference, including introductions to causal thinking and do-calculus. Rajamanickam's work aims to advance the ability to draw reliable causal conclusions from observational data, a critical challenge in many scientific disciplines. His total publication count stands at 20.
Metrics
- Publications: 20
Selected Publications
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Backdoor and Frontdoor Criteria (2025)
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Introduction to Causal Thinking (2025)
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Summary of Key Concepts (2025)
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Propensity Scores in Causal Deep Learning (2025)
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Balancing Representations with Causal Deep Learning (CFRNet) (2025)
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Causal Graphs: Structure and Assumptions (2025)
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Causal Estimation Basics (2025)
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Introduction to Do-Calculus (2025)
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Case Studies (2025)
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Assumptions and Real-World Challenges in Causal Inference (2025)
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Evaluating Causal Models Without Counterfactuals (2025)
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Interventions and Counterfactuals (2025)
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Solutions to Exercises (2025)
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Treatments, Outcomes, and Confounding: Core Concepts (2025)
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Causal Inference Meets Deep Learning (2025)
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