Experimental Methods
===================

Experimental methods represent the gold standard for causal inference. When randomization is possible, these methods provide the strongest evidence for causal relationships.

.. toctree::
   :maxdepth: 2

   randomized_controlled_trials
   ab_testing
   field_experiments
   natural_experiments

Overview
--------

Experimental methods leverage randomization to ensure that treatment assignment is independent of potential outcomes, eliminating selection bias and confounding. This makes causal identification straightforward and assumptions minimal.

**Key Advantages:**
* Strongest causal evidence
* Minimal identifying assumptions
* Clear interpretation of results
* High internal validity

**Key Limitations:**
* May not always be feasible or ethical
* External validity concerns
* Potential for spillover effects
* Implementation challenges

Method Details
--------------

**Randomized Controlled Trials (RCT)**
   The gold standard for causal inference with random treatment assignment.
   
   * **When to use**: When randomization is feasible and ethical
   * **Key assumption**: No spillover effects between units
   * **Strengths**: Strongest causal evidence, minimal assumptions
   * **Limitations**: May not be feasible in all contexts

**A/B Testing**
   Specialized form of RCT commonly used in technology and marketing.
   
   * **When to use**: Digital products, marketing campaigns, user interfaces
   * **Key assumption**: Stable unit treatment value assumption (SUTVA)
   * **Strengths**: Easy to implement, fast results, scalable
   * **Limitations**: Limited to contexts where randomization is possible

**Field Experiments**
   RCTs conducted in real-world settings rather than laboratory conditions.
   
   * **When to use**: Policy interventions, social programs, real-world contexts
   * **Key assumption**: Randomization integrity maintained in field
   * **Strengths**: High external validity, realistic conditions
   * **Limitations**: More complex implementation, potential contamination

**Natural Experiments**
   Situations where treatment assignment is "as good as random" due to natural processes.
   
   * **When to use**: When true randomization isn't possible but natural variation exists
   * **Key assumption**: Treatment assignment is exogenous
   * **Strengths**: Combines experimental logic with observational data
   * **Limitations**: Requires careful validation of randomization assumption

Implementation in CAIS
-----------------------

CAIS automatically detects experimental designs and applies appropriate analysis methods:

.. code-block:: python

   from causal_agent import CausalAgent
   
   # CAIS automatically detects RCT design
   agent = CausalAgent()
   result = agent.analyze(
       data=rct_data,
       treatment='randomized_treatment',
       outcome='outcome_measure'
   )

**Automatic Detection**
   CAIS identifies experimental designs by checking for:
   * Random treatment assignment patterns
   * Balanced treatment groups
   * Experimental design indicators in data

**Analysis Features**
   * Randomization balance checks
   * Intent-to-treat (ITT) analysis
   * Treatment-on-treated (TOT) analysis
   * Subgroup analysis and effect heterogeneity
   * Power analysis and sample size calculations

Best Practices
--------------

**Design Phase**
   * Clearly define treatment and control conditions
   * Ensure adequate sample size for desired power
   * Plan for potential attrition and non-compliance
   * Consider stratified randomization for balance

**Implementation Phase**
   * Maintain randomization integrity
   * Monitor for spillover effects
   * Document any deviations from protocol
   * Collect rich baseline data

**Analysis Phase**
   * Check randomization balance
   * Conduct intent-to-treat analysis as primary
   * Consider treatment-on-treated analysis if relevant
   * Explore effect heterogeneity responsibly
   * Report results transparently

Common Challenges
-----------------

**Ethical Considerations**
   * Ensure treatments are beneficial or at least not harmful
   * Obtain proper informed consent
   * Consider equipoise and clinical uncertainty
   * Plan for early stopping if needed

**Implementation Issues**
   * Maintaining randomization integrity
   * Preventing contamination between groups
   * Managing attrition and non-compliance
   * Ensuring adequate sample sizes

**Analysis Complications**
   * Handling missing data appropriately
   * Dealing with non-compliance
   * Multiple testing corrections
   * Interpreting null results