5 papers
Causal Inference with Unstructured Outcomes
Kevin Christian Wibisono, Yixin Wang
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasin…
Causal Inference with Unstructured Treatments
Kevin Christian Wibisono, Yixin Wang
Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instruc…
Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model
Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee +1
In many practical situations, randomly assigning treatments to subjects is uncommon due to feasibility constraints. For example, economic aid programs and merit-based scholarships…
Exponential Family Attention
Kevin Christian Wibisono, Yixin Wang
The self-attention mechanism is the backbone of the transformer neural network underlying most large language models. It can capture complex word patterns and long-range dependenci…
From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When
Kevin Christian Wibisono, Yixin Wang
Large language models (LLMs) like transformers demonstrate impressive in-context learning (ICL) capabilities, allowing them to make predictions for new tasks based on prompt exempl…