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ORCIDHarsh Parikh
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2020 – today
- 2024
[c8]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Safe and Interpretable Estimation of Optimal Treatment Regimes. AISTATS 2024: 2134-2142- [c7]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. AISTATS 2024: 3340-3348 - [i13]Harsh Parikh, Rachael Ross, Elizabeth A. Stuart, Kara E. Rudolph:
Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population. CoRR abs/2401.14512 (2024) - [i12]Melody Y. Huang, Sarah E. Robertson, Harsh Parikh:
Towards Generalizing Inferences from Trials to Target Populations. CoRR abs/2402.17042 (2024) - [i11]Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, Sudeepa Roy, Babak Salimi:
Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects. CoRR abs/2403.11332 (2024) - 2023
- [c6]Harsh Parikh:
Synthetic Controls as Balancing Scores. Tiny Papers @ ICLR 2023 - [c5]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
Variable importance matching for causal inference. UAI 2023: 1174-1184 - [i10]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR abs/2302.11715 (2023) - [i9]Marco Morucci, Vittorio Orlandi, Harsh Parikh, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
A Double Machine Learning Approach to Combining Experimental and Observational Data. CoRR abs/2307.01449 (2023) - [i8]Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky:
Estimating Trustworthy and Safe Optimal Treatment Regimes. CoRR abs/2310.15333 (2023) - [i7]Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. CoRR abs/2312.10569 (2023) - 2022
- [j2]Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
MALTS: Matching After Learning to Stretch. J. Mach. Learn. Res. 23: 240:1-240:42 (2022) - [c4]Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen:
Validating Causal Inference Methods. ICML 2022: 17346-17358 - [i6]Harsh Parikh, Carlos Varjao, Louise Xu, Eric Tchetgen Tchetgen:
Evaluating Causal Inference Methods. CoRR abs/2202.04208 (2022) - [i5]Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun
, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover:
Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. CoRR abs/2203.04920 (2022) - [i4]Harsh Parikh:
Are Synthetic Control Weights Balancing Score? CoRR abs/2211.01575 (2022) - 2021
- [i3]Amir Gilad, Harsh Parikh, Babak Salimi, Sudeepa Roy:
Detecting Treatment Effect Modifiers in Social Networks. CoRR abs/2105.10591 (2021) - 2020
- [c3]Babak Salimi, Harsh Parikh, Moe Kayali, Lise Getoor, Sudeepa Roy, Dan Suciu:
Causal Relational Learning. SIGMOD Conference 2020: 241-256 - [i2]Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu:
Causal Relational Learning. CoRR abs/2004.03644 (2020)
2010 – 2019
- 2018
- [i1]Harsh Parikh, Cynthia Rudin, Alexander Volfovsky:
MALTS: Matching After Learning to Stretch. CoRR abs/1811.07415 (2018) - 2016
- [c2]Georgios Tsolaridis, Epameinondas Kontos, Harsh Parikh, Ruben M. Sanchez-Loeches, Remus Teodorescu, Sanjay K. Chaudhary:
Control of a Modular Multilevel Converter STATCOM under internal and external unbalances. IECON 2016: 6494-6499 - 2015
- [j1]Harsh Parikh, Apoorvi Singh, Annangarachari Krishnamachari, Kushal Shah:
Computational prediction of origin of replication in bacterial genomes using correlated entropy measure (CEM). Biosyst. 128: 19-25 (2015) - [c1]Alok Shankar Mysore, Vikas S. Yaligar, Imanol Arrieta Ibarra, Camelia Simoiu, Sharad Goel, Ramesh Arvind, Chiraag Sumanth, Arvind Srikantan, Bhargav HS, Mayank Pahadia, Tushar Dobha, Atif Ahmed, Mani Shankar, Himani Agarwal, Rajat Agarwal, Sai Anirudh-Kondaveeti, Shashank Arun-Gokhale, Aayush Attri, Arpita Chandra, Yogitha Chilukur, Sharath Dharmaji, Deepak Garg, Naman Gupta, Paras Gupta, Glincy Mary Jacob, Siddharth Jain, Shashank Joshi, Tarun Khajuria, Sameeksha Khillan, Sandeep Konam, Praveen Kumar-Kolla, Sahil Loomba, Rachit Madan, Akshansh Maharaja, Vidit Mathur, Bharat Munshi, Mohammed Nawazish, Venkata Neehar-Kurukunda, Venkat Nirmal-Gavarraju, Sonali Parashar, Harsh Parikh, Avinash Paritala, Amit Patil, Rahul Phatak, Mandar Pradhan, Abhilasha Ravichander, Krishna Sangeeth, Sreecharan Sankaranarayanan, Vibhor Sehgal, Ashrith Sheshan, Suprajha Shibiraj, Aditya Singh, Anjali Singh, Prashant Sinha, Pushkin Soni, Bipin Thomas, Kasyap Varma-Dattada, Sukanya Venkataraman, Pulkit Verma, Ishan Yelurwar:
Investigating the "Wisdom of Crowds" at Scale. UIST (Adjunct Volume) 2015: 75-76
Coauthor Index
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