Datasets:
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"field_id": "F030",
"field": "causal machine learning",
"directions": [
{
"direction_id": "G0",
"name": "Latent causal modeling and counterfactual generation",
"scope": "Establish causally meaningful latent variables, mechanisms, or observation mappings from complex observations, and study the interventions and counterfactual observations supported by these models."
},
{
"direction_id": "G1",
"name": "Causal structure discovery",
"scope": "Infer or disambiguate causal directions, relations, and structures among variables using evidence from observational statistics, temporal dynamics, interventional environments, or knowledge provided by language models."
},
{
"direction_id": "G2",
"name": "Causal-effect identification and bounds",
"scope": "Determine the extent to which available distributions and causal assumptions establish a target causal quantity, and provide an identifying expression or a range when point identification is not possible."
},
{
"direction_id": "G3",
"name": "Causal-effect estimation across data and treatment settings",
"scope": "Use nonrandomized observations, auxiliary variables for identification, longitudinal records, donor or external-population evidence, and data from interacting units to estimate treatment outcomes, effects, and counterfactuals for target populations or units."
},
{
"direction_id": "G4",
"name": "Causal intervention and sequential policy learning",
"scope": "Use information about causal effects or mechanisms to choose actions and learn allocation rules, sequential interventions, or agent policies in environments that improve outcomes."
},
{
"direction_id": "G5",
"name": "Causal experiment design and inference",
"scope": "Design experiments or analyze experimental data under randomized assignment, adaptive implementation, and sampling constraints to obtain valid and precise causal estimates."
},
{
"direction_id": "G6",
"name": "Validation of causal models, methods and reasoning",
"scope": "Examine the reliability of causal models, estimation methods, or causal judgments made by language models, and analyze the conditions under which they apply or fail."
},
{
"direction_id": "G7",
"name": "Causally robust prediction",
"scope": "Use causal mechanisms, invariance, or interventional information to keep predictors effective under distribution shifts and spurious associations."
},
{
"direction_id": "G8",
"name": "Causal fairness",
"scope": "Define and improve the fairness of predictions and action allocation using causal disparities, counterfactuals, or intervention benefits."
},
{
"direction_id": "G9",
"name": "Causal explanation and root-cause diagnosis",
"scope": "For a specific output or an anomaly that has occurred, determine which factors or changes caused the result and provide interpretable causal attribution."
},
{
"direction_id": "G10",
"name": "Intervention-preserving causal abstraction",
"scope": "Simplify causal models or relate different levels of abstraction while preserving the required consistency of interventional behavior."
}
]
}
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